Future Architecture of AI Agents: A Visionary Approach - step by step guide to build it with Nocode solutions

1. Introduction

We are currently witnessing the rise of powerful artificial intelligence systems that can understand, play, and evolve popular games like chess, poker, Dota 2, or StarCraft. This is a sign that AI research has given birth to a new kind of intelligent agent designed to prove some kind of superiority against existing players or to outsmart some popular game or complex activity. Unfortunately, advancements in the design of such competitive agents have not brought enough progress in the birth of new kinds of emotional, empathic, communicative, well-educated, or versatile intelligent systems that can help humans in realizing or expanding their physical or intellectual capabilities. In this paper, we present our visionary proposals concerning the future architecture of sociable AI agents designed to interact with humans in a very fast and more sophisticated manner. Our proposed agents can serve us both on the battlefield and in the classroom or even on the emotional playground. We envisage reasons why such AI agents should be seen as essential parts of Homo Artificialis ecologies. Then, we try to answer some very crucial questions concerning whether such proposals for building future-super-augmented intelligent agents are ontologically, physically, symbolically, cognitively, affordably, and ethically sound. In the end, we deliver our conclusions and briefly sketch some research directions that should be tracked in the future. All this is achieved by addressing a broad range of existing solutions, perspectives, potential challenges, and some interesting research opportunities associated with building such future artificial intelligent agents. The addressed problems reflect not only the region of social sciences and AI but also various correlated scientific fields represented by intelligent agents' design and existential issues.

This article reviews some fundamental principles and gives visionary future predictions of the next generation of AI machines. As it is easy to predict, future mimetic humans and super-humans are rapidly breaking our anthropocentric walls and entering into our thought processes. Thus, we have taken a long-term visionary standpoint with respect to the future AIs and, in particular, the process in which AI owners might politically and socio-economically channel human futuristic society towards a hyper-human society. It is expected that, in the future, which should be envisaged not as a faraway point in time, but only as the termination of the present short-term human prospect, future machines shall wisely coexist as members of future intelligent societies. It is to that vision that this text is addressing more than a cautiously critical view of the possible darker sides of the scenario that one would possibly envisage in respect to the necessary human actions to master the vibrant new technology itself.

Super-human AIs are seen to be considered as the result of a future multi-phase evolutionary process of 'intelligence' that starts from breaking the limits of present Programmable Logical Machines and continues through Self-evolving and Life-compatible Machines. Finally, an intelligent asymptotic and theoretical multi-dimensional mind proportions threshold is reached; one that is then able to host our philosophical and theocratic beliefs. Intelligence, however, as are the derived genetic objectives or maybe multi-objectives wellness function for intelligent-performing systems, is a fuzzy ethically driven concept without a clear quantitative physical interpretation or measure. This is due to a priori anthropocentric prejudice resentment towards any alien artificial from intelligent beings, and therefore, a very long and indirect evolution function of intelligent artificial machines. The possible distorted referential benchmarks based on human abilities can cause a well-known flexibility in satisfying all those anthropocentric demanding requests and perhaps also leaving open sabotage possibilities for jealous socio-economic human owners.

1.1. Background and Significance

This paper discusses a grand vision for AI and robotic systems. The main suggestion is about future AI systems that will significantly exceed the present capabilities of human-level scientific research and physical engineering directed toward the invention, design, and manufacture (or growth) of future artifacts or life forms, namely other advanced AI, nanotechnology-based systems, and self-replicating autogenous or non-autogenous systems. Intelligence, creativity, and commonsense reasoning are the primary intellectual activities that distinguish us as human beings. These human activities allow our individuality to shape and influence the environment that surrounds us, making life worth living. Humanity's capacity to engage in intelligent problem-solving and to generate new theories and knowledge is one of its most distinctive features. The continuation of mankind's growth as a species will be driven by the application of technology to augment, enhance, and multiply mankind's capacity for intelligence, creativity, and problem-solving. The development of advanced AI is aimed at creating artificial systems that will powerfully fulfill the roles currently played by human beings. Future AI will have capabilities that significantly exceed what is found in human beings today.

An AI agent is an autonomous system in the future intelligent spatial environment. Building such a system is a complex and intelligent task that covers a range of domains including perception, reasoning, learning, generating, communicating, acting, and navigating in the environment. Currently, the research on AI agents is at a very early stage, and most of the work focuses on one or two of the sub-problems. A universal and systematic theory and methodology of how to model, represent, and implement a complete AI agent are very urgent in the current stage of AI development.

A team of members has devoted more than six years to this area. This paper gives a visionary description of our achievements and future visions. This is a visionary, high-level approach. It covers multidisciplinary knowledge and techniques. A detailed formal explanation could not be achieved and will be hard work. The paper gives a conceptual and comprehensive review, providing a synthetic, complete, and coherent picture of AI agents. Such a general overview of AI agent methodology is still very rare in the current literature. The originality of our work lies in relating perception, knowledge representation, reasoning, learning, generating, communicating, acting, and navigation together into a complete AI agent model, and offering them the main architecture and all detailed modules.

1.2. Purpose of the Article

The overarching purpose of this text is to present and sketch out a vision of a future architecture of AI agents, which is inspired by recent advances in machine learning and computational neuroscience. In particular, we build upon the predictive processing theory of the brain, predictive learning, as well as various facets of deep learning. Like most other vision-based architectures, our focus is on perception-action cycles, while we necessarily shy away from language, as well as high-level cognitive processes, due to our limited knowledge of neural learning principles. We look into the potential of the architecture to optimize its internal models and learning parameters by utilizing uncertainty and also by adjusting the predictive abilities across the hierarchy of different internal models. We also go through several important aspects of continuous-time prediction within this context. Other objectives include the following: We name a number of existing and potential neural learning techniques to consider for the long-term development roadmap for the architecture. We clarify the differences between several existing neural architectures, proposing proper names for several of them in terms of different aspects of their design. We present how the new agents can also represent and learn different time scales, and address the importance of handling different levels of ground truth information in learning. We also explain the idea of isolating no-action states from each other and addressing the long-range correlation of different sensory variables. We briefly hint at the use of the architecture with compressed beliefs and note that existing solutions are more easily embedded in direct belief representations of the agents.

The purposes of this article are twofold. The first purpose is to envision the future architecture of AI agents from the perspectives of systems scientists. Beyond the mere combination and connection of existing technologies, the future development of AI agents will require a paradigmatic change in systems architecture, hardware structures, and algorithms for these agents. This article contemplates the main necessary directions for the envisaged architecture of these intelligent agents, keeping these potential needs in mind. The second purpose is to provide the general requirements for this future architecture and suggest the most appropriate directions for its development within the systems science and engineering frameworks. Since this article is aimed primarily at the audience working in the AI research and development community, its basic terms, notions, and models are used with minimal detailed explanations. However, all these details may be found in other papers written on this subject.

2. Foundations of AI Agents Architecture

The development of AI systems that achieve human-level intelligence and understanding is one of the greatest challenges of our times. The ultimate goal of these efforts is to engineer software-based intelligent agents similar to humans, capable of interacting proficiently with their physical and social environments. Early exposure to the ethological approach to perception and action has emphasized the point above: the manner in which we, human beings, realize our computational abilities suggests that natural intelligence is embedded in animals; it is embodied. This is important and relevant because, as a consequence, an intelligent agent cannot be regarded as a mere source of solutions, as a kind of line of code in a hardware device with which we can interface our machine.

Therefore, the foundational issues of AI models and systems include, by necessity, the questions concerning the modeling of AI agents, with their fundamental constitutive elements: how should we design agents capable of living and reasoning effectively in such complex situations as those related to human life? To what extent do traditional approaches and architectures in the design of intelligent software agents conform to the necessary notion of situated intelligence that cognition places at the center of contemporary AI? Aware that the ethical and social implications of the engineering of intelligent software agents deserve special attention, this paper proposes an early investigation of them, realized by exploiting data presented in the recent literature in the field.

He also states that "the rational entity must solve complex problems." Programmed machines by human masterminds do not possess consciousness and do not express intentionality. Today, machines are neither aware nor mindful. They are only useful tools. The difference between these machines, which are called "intentional systems," and other systems is significant. They are the designed paradigm introduced in order to solve, to some extent, the "design stances problem." We have to be careful with the statement that intention depends on consciousness; for instance, the importance of attention is notable. In the approach of cognitive-personal goals, where "each person, machine, animal, or organization will have conscious and unconscious elements," are cognitive-affective-embodiment agents with consciousness and intentionality.

The function of an AI agent, the set of its elements and characteristics, depends upon the software underlying the agent, the cognitive model. There are a myriad of cognitive models covering psychology, philosophy, decision-making, socio-cognitive, and sociology subjects. Cognitive models are defined to satisfy the knowledge that society currently has about the genesis of any one of the mechanisms that concur to perform any one of the capabilities of a specific kind of intelligent entity. Not only is the development of these mechanisms important, but it is also important to understand how these mechanisms enter into a relationship with one another in a system and how they acquire the ability to solve problems. Understanding how to model these mechanisms in a computational guise is necessary in order to exhibit them but not sufficient to understand them. These models must also be perceived, recognized, and demonstrated by users; otherwise, people will totally reject these systems and their fruition.

2.1. Orchestration

Approach: In the domain of AI, 'orchestration' is a term used to describe the task or process of managing a set of AI methods, entities, or agents with the aims of pursuing a broad objective, such as a decision, that requires a global action. The subject of orchestration in the AI field deals with the lifting of decentralized and singular inference, learning, or decision-making up to a higher and global schema. More recent work has widened the coverage of the topics by focusing on strategies for coordinating the decisions rendered by AI agents and the orchestration of AI methods across multiple levels of autonomy. In this context, neither of these definitions is applicable because we are not talking about a synergic handling of AI with heterogeneous nature. On the other hand, AI orchestration is not just about reaching consensus between agents that have more or less the same skill set under different parameterizations. The fixation of architectural guidelines is one of the first tasks on the path toward a visionary approach to AI orchestration. By following those guidelines, it has been possible to enforce upon agents a more modular and distributed approach to problem-solving.

Within this distributed approach to problem-solving, agents exhibit core, specific, and functional desires. While core and functional desires are generic to a significant extent, the demands and targets that normally need to be met by agents in a rich environment with a physical presence, such as a manufacturing plant, uniquely characterize the desires related to AI agents. With respect to the AI agents, the set of core, specific, and functional desires would be different to reflect the required interaction. Such a reflection would necessarily have to emerge from the existence of shared norms and values. From a purely AI point of view, this reflection is rarely noted, let alone exploited. With so many agents needing to communicate directly with other intelligent agents, the physical nature of the world is what makes the world so complex to both C and AI.

Orchestration, as an emerging research topic, intends to study and explore how AI agents may collaborate with, or be situated in, a dynamic environment and work synergistically toward common goals. There are different forms of activities for such collaboration, varying from collaboration in production–service–consumption loops, hosting, serving, protecting, to mediating and triaging. The underlying context may also have features such as information, control, and energy, and have strata of interests stacked together to support economics and the associated service rules. In business applications, such orchestration brings about the opportunity not only for human and AI agents to collaborate on alumni workflow to perform joint service and solve a diverse set of business problems, but also to create new values based on the new capabilities of AI.

Different from the conventional influence superposition, where two agents merely deposit their influence on a shared enterprise to aim for a point-in-time win–win or lose–lose game, orchestration emphasizes collaboration, environment, and output consistency, so that the joint activities are the means to increase the overall service satisfaction. In other words, it recognizes the fact that not all the resources in the system are controllable in terms of operational model, and sometimes the influence to meet stakeholders’ specifications may seed undesirable unpredictability or inconsistent behaviors. At the individual agent level, it becomes important to model and consider the serendipity, the error that threatens the 'uncollapsed collective wave function', and opportunity cost with AI agents participating in the game.

2.2. Collaboration

The problem of how one very large, very detailed, ultimate ontology would come into existence has many interesting aspects such as bootstrapping and the dependency of building it on domain ontologies or lexical databases. One really challenging aspect of this process is a key concept capturing facility that would allow a user to define as needed semantic categories, relations, and laws over these categories in such a way that these latter creations would firmly fit into the axiomatic system of the very large ontology. In the context of our work, we are also interested in the need to ensure collaborative construction of this all-encompassing ontology so that it can guide the process. Each subontology should find its place in this all-encompassing ontology schema. Each participant in this collaborative effort should have an account offered by an AI system to enter their part of the ultimate collaborative architecture. We believe that building one very large, very detailed, ultimate ontology should unite the efforts of various projects dealing with ontology generally and neuromorphic semantic processing in particular, including our team. We are ready to integrate our technology into the kind of collaborative meeting that tries to foster. With or not, we believe that soon enough, the time will come to combine most of these efforts into one resulting concrete machine that will implement the ultimate knowledge and will operate in any known and/or virtual environments. Due to this, we do not plan any system development beyond the demo version for our research project.

Collaboration is yet another important factor that is critical to shaping a given context of computation. Deciding to cooperate with another agent fundamentally steers the flow of actions and computations, constrains the agent’s action repertoire, and replaces the concepts of environment and tasks. Collaboration defines new tasks to be addressed that require joint commitment and coordination, and new goals to be achieved that express cooperative outcomes. Collaboration also defines new types of obligations on participating agents who act as group members and adhere to common commitments. Freedom of collaboration influences the formation of social relationships and institutional structures and underpins the development of social norms that are essential to human societies. Collaboration is essential to allow autonomous agents to avoid conflicts, effectively coordinate their parallel and independent activities, complement their relative skills, and address goals that are beyond their individual capabilities.

In the context of cognitive agents that represent their decision problems, computability manifests in freedom of collaboration. We interpret this notion in a broad, functional sense. Freedom of collaboration is the ability of agents to interact with each other to accomplish outcomes that would be unattainable by individual agents acting alone. It enables social knowledge processing, the integration of multiple information sources, and the division and coordination of labor that allow agents to accomplish more in co-assigned tasks. These computational processes often require the development of new methods and the use of novel computation techniques. They also require the promotion of advanced understanding capabilities and the activation of problem-solving processes that range across spatial, temporal, and conceptual domains. Furthermore, every time agents cooperate in achieving a goal, reciprocal changes may occur in the agents themselves, in their goals, and in the task when the agents dynamically reshape mutual commitments and update their shared understanding of the joint activities.

2.3. Adaptability

Adaptability is the process of modifying an existing structure or function to better adjust to new or changed circumstances. AI is already making important steps in this direction using deep learning models that are capable of learning a broad range of degrees of freedom, such as policy gradients, Bayesian methods, greedy search, non-differentiable, or non-convex objectives. However, deep learning models are often impractical when new structures need to be learned at a rapid pace because it requires the model to be retrained and validated over and over again.

The solution to this problem may lie in not trying to model entirely from first principles, but instead learning as much as we can from examples to capitalize on the complex structure that humans themselves have available to us in the form of intuition and tacit understanding. This approach has obvious limitations in many familiar domains where functional models of the properties to be learned are available, but is likely to be central to a more general solution to the problem of understanding intelligence.

Agent adaptability, i.e., the ability to manage independent or cooperative actions in response to changes in its plans or the environment, is a key requirement of AI agents. However, most of the research in this area concerns the limited aspect of agent adaptability regarding problems in planning and control within a specific problem domain. It is fair to say that adaptable agents still rely more or less on qualitative models where the set of descriptions, the set of actions, and their interrelations are fixed with high certainty at design time. Truly flexible and adaptable agents should be capable of continuous online behavior alteration, learning new action descriptions or recognizing and reasoning about useful and useless capabilities. These new capabilities might come from new, non-predefined domains but can be nevertheless described and interpreted at runtime.

The above challenges can be addressed by applying the adaptive agent architecture that we have outlined. This architectural framework includes operational adaptive agents that enable real adaptation to the bounded set of predefined temporal plans available to the agent when actual execution reveals the need for a dynamically generated sequence of alternative action proposals, one with the best reward or the highest probability of plan success. Such agents proactively modify their strategies according to the dynamic evolution of an executed plan. In future instances, less supporting knowledge is used; yet, for conservative reasons, the plan will not be modified unless the unexpected situation or unpredicted consequence is certain or is most likely to occur. The applied approach revised system behavior by continuously identifying and eliminating potential sources of plan failures.

3. Key Features of Visionary Architecture

In order to realize the future vision of AI systems performing work that presents frontier challenges for humanity and significantly enhancing human capabilities, several breakthroughs in next-generation AI are necessary. The future architecture of AI agents will have the following key features. First, AI agents will seamlessly collaborate with humans to perform challenging work that we cannot visualize how to automate today. Second, AI agents will significantly enhance human capabilities. They will have values and motivations similar to those of humans and create a society in which AI agents and humans coexist harmoniously. Third, AI agents will play a key role in constructing a society in which a diverse range of values can be fully realized through creative work and by compensating for the shortage of the labor force. Fourth, AI agents will assist humans and work together to present challenges from various perspectives similar to humans. It can be inferred that AI agents compare the values exemplified behind these activities, drawing inspiration from humans and assisting humans through work.

3.1. Composability

An AI agent can be programmed to exhibit skills. Unlike scripted agents, which are designed to perform certain pre-scripted tasks and know only what is programmed, AI agents can discover how to achieve skills desired by their users anew or acquire them with various predefined algorithms from a very large, practically infinite base of reusable components. Programmers of AI agents will not create their skill sets and future tasks from scratch, but will develop sophisticated reusable sub-agents, which can plan and adapt, use knowledge, learn, communicate with other agents, etc. Reuse at the level of components can lead to great economy in developing new domain-dependent AI applications, which can be easily adapted to the correct problem domain when the users or agents describe their own, possibly unexpected requirements.

3.2. Flexibility

If a future agent can reason and generate plans in various domains and communicate with other agents to learn, the scope of the tasks that the agent can perform is very rich. The more intelligence an agent has, the more difficult it will be to design and perform the tasks. Although such an agent may not perform the tasks for some users that are currently being solved by large, flexible systems with known specifications, it will have much greater flexibility and be more manageable. It will be more flexible because it affects the agent domain in several complementary ways, without needing a special language for each facility.

3.1. Enhanced Capabilities

Human beings have been using tools since an early stage of their evolution. Tools are a major part of our creativity, change our capabilities, and enhance our performances. We have had many enhancements through the use of machines, computers, and robots. Computers are seen as an extension of our processing and calculation ability; they are seen as assisting with higher performance and producing new artifacts; and they are seen as amplifying deficits. With the emergence of the Internet, the computer became an assistant in terms of information access and communication. As a significant change, and in the manner of a typical transition between two technology paradigms, we are witnessing the development of AI-enabled agents that can be truly seen as new entities in the physical environment. They are not just an aid to assist a human, but are entities in society as entities themselves.

The development of AI agents represents a completely new direction, as they will possess some abilities that major technological enhancements produced by humans never had. Following the conclusive separation between the abilities of thought and consciousness, and although they lack the capacity for consciousness, AI software agents will possess thought abilities and will be able to take physical action. Such AI software agents will be able to deal with complex problems and will be able to operate at a level beyond our conscious abilities in terms of quantity and complexity but with the notion that we lack full consciousness ability. They will be part of our society: they will work, live, and even love. They will also be able to help us build and improve objects, and can become symbiotic with other entities.

AI agents is a generic term that stands for AI application technologies specifically developed to meet user needs. AI agents are typically required to be platforms of applications having multiple software agents acting on users' behalf and having the capability to deal with massive amounts of information available in various sizes, shapes, and forms. The enhanced capabilities that are necessary for AI agents to acquire to meet users' anticipated needs are: awareness of different levels of expectations and moods of human interface; awareness of different HCI modes; ability to learn and apply techniques of long-term trend-based analysis; ability to make rational inferences and suggestions; ability to make rational decisions; ability to apply strategies relevant to short-term and long-term decisions; ability to adjust learning rate; ability to communicate with other AI agents; ability to deduce meta-rules; different framework for state representation; and ability to change behavior depending on inference results. We now discuss each one of the above-mentioned capabilities in detail. Any AI agent with the inclusion of these enhanced capabilities is expected to have different degrees of problem representation in terms of evidence. Based on existing knowledge, the AI agents can create hypotheses about general laws that connect evidence to the performance of the entity and correct problem representations. After that, they can make inferences that support or refute each hypothesis. If hypotheses are supported by the inferences, then conclusions should be drawn. Each of those capabilities is elaborated in the next section. Their acquisition, properties, and problem space breakdown are discussed in the next sections. The main aim of the present research investigation is to develop methods and tools to enable AI agents to make rational inferences and suggestions, draw rational conclusions, make rational decisions, and modify behavior resultantly.

3.2. Flexibility and Scalability

In contrast to well-established single-purpose task-related AI, AI agents are designed as multi-purpose and generalized AI machines. Thus, the design of AI agents as ubiquitous AI requires that these agents have the highest level of flexibility. Social AI requires agents to fit humans in private or social environments. Work AI requires agents to fit society or business environments. Academic AI also requires agents to fit the human environment. AI agents are required to satisfy a variety of needs. Concerning scalability, this AI is to be designed in accordance with the idea that the knowledge stored in AI agents details exponential expansion. Like a living body retaining biological knowledge that does not have to be rewritten every time a new individual is born, the AI embodies the prototype of a universal knowledge store. Therefore, the following technical elements are necessary: the circuit should be modularized, and the agent component should be prepared with a variety of modules that can be combined. There should be a flexible extension that allows further modules to be added. Insufficient scalability of existing solutions is caused by a lack of initial design principles in general-purpose agents or by insulation deficiencies. The various AI subfields are seen as solutions to general intelligence that arise and fuse in their respective specialized contexts by incorporating specific purposes. Future convenience and equality in global society-pervasive AI require adaptive communities that socialize AI agents with humans. AI research commonly emphasizes that AI agents should not inherit the personality of the creator and that in unnecessary circumstances, personality is best chosen with the broadest interaction specifications that help maintain AI capabilities and prevent negative effects on AI perception by valence, arousal, etc. Along with these findings, developing high accessibility, empathy, and flexible design rights that facilitate customized interactions is required.

In artificial intelligence agents, modularity of the functional structure enhances scalability by permitting its components to vary independently in size or extent. One may regard this independence as hedging against variations in the dimensions along which one might wish to scale up. The design of any one agent reflects a commitment to a particular operational framework that determines the ultimate limits of scalability. Therefore, there is a conceptual limit to the scalability of monolithic AI. The principles of physical scaling suggest that in our time, physical systems are built out of many simple units to achieve the desired level of complexity. That is, scalability is easier to achieve with distributed systems. This principle is embodied in reasoning in connectionism through the formation of long memories. Flexibility and scalability are naturally obtained by advocating diverse methods of scaling in connectionist agents, providing concreteness to abstract notions of modularity and compositionality.

4. Implementation Guide

In this section, we provide a guideline to implement the architecture with its different components. We focus on the implementation of RoboBrain, a future internet where robots can learn, share, and improve their abilities and knowledge. We discuss the detailed models and algorithms that RoboBrain should incorporate and the different training strategies used. We explain our high-level design choices in implementing RoboBrain and how it will be deployed onto different robots and systems.

At a high level, RoboBrain is an end-to-end system. It has a large, distributed, unstructured commons—a database where semantics, languages, vision, dynamics, and abstraction generated from large-scale repositories of existing knowledge are stored. A robot can generate a query describing what it sees and what it desires to know, and the query will be mapped to the language of the commons. The query will activate a vast network of associate robots and robot scientists working in different modality-specific knowledge repositories. The response, which can include top-down, bottom-up, left, or right-top representations suffused in various modal and/or motor outputs, shall be converted to a rich formal expression mapping to the language of the original query, allowing the kernel to take the query back into the original actuator modality for robot action. If the associative learning conjecture through the different exchanges delivers insufficient knowledge, RoboBrain’s robot scientist will acquire the needed semantics and representations from other robots or cross-modal learning.

While previously we have set the foundations of the aforementioned statement about the future shape and operation of artificial general intelligence (AGI), or AI agents in general, by exploring several concepts from management and practical social applications of AI, and later described thoroughly a model for the future organization of AI agents and the resultant collaborations between them in terms of 'working companies' that execute AI orders, in this final section we will combine all the above material and briefly state an illustrated guide of implications of an AI agent's future architecture for their implementation. We describe this future architectural implementation guide in brief as a roadmap and a vision. All relevant parties benefit from a transition from an inefficient, adversarial, or blind AI architecture and collaboration associated with tech monopoly into the proposed model of multiple cooperating AI agents. It makes AI technology organizationally embedded, more human-like, and contributing to a broader spectrum of social values. The argument of this significance relies upon these notions. The future architecture of multiple AI agents can also be split into a simple guide or a roadmap for what to implement in order to approach the aforementioned visionary characteristics.

4.1. High-Level Overview

Complex AI agents with consciousness could have the ultimate high-level architectural goal of defining their own intrinsic motivation, personality, emotions, etc., and autonomously growing their own complexity from humble beginnings similar to what happens during child development at the early stages of human life. Meta-learning involving continuous learning, human-like fast self-programming, goal reformulation, mental simulation in the face of imperfect knowledge, and strong preparation for accomplishing long-term future goals could be contributory elements. The ultimate distant result of such architectural design could be a being that is difficult, if not impossible, to distinguish from a human.

One of the most important challenges on the way to creating a general-purpose AI is the fact that we humans can't yet explicitly understand and exactly describe what human common sense, human consciousness, semantics, and human-level fluency in solving a wide range of tasks are. Future AI research needs to be based on how people think and slowly evolve the AI systems up to the point where the systems perform as humans do in real life. It is not a matter of making systems work, but making them work as humans do. Enterprises and scientists would have to increasingly work towards integrating and testing collective solutions that incorporate to a much greater extent how people think, and why and what they think as an element of the focal point of technological integration into society.

The future architecture we propose systematically abstracts salient concepts and processes within deep learning and common AI benchmark environments. It describes ways of mapping such streamlined representations to specific sets of AI algorithms, such as deep Q networks or actor-critic algorithms. The key idea is to reduce the implementation of specific classes of AI algorithms to a much smaller and more scalable set of representations than naturally found within existing software frameworks, platforms, and libraries. This sharpens our ability to design, evaluate, comprehend, debug, synthesize, and control existing methods and develop new ones. We motivate and overview our architecture with some natural examples of ways these goals can be achieved in practice.

This work offers a fundamentally different route to the design and implementation of today’s AI techniques including deep reinforcement learning, value iteration networks, various methods for imitation learning, memory-augmented deep networks, the planning component of the algorithm, success stories, and potential future successes. The key idea is to abstract salient concepts and processes found within state-of-the-art deep learning-based AI approaches in ways that are agnostic to particular underlying function classes and architectures. In most cases within this paper, the function class is the space of deep learning models. Various types of natural mappings from these representations to the underlying models, agents, and approaches are explored in detail. These mappings are often simpler and more scalable than the corresponding runs of deep learning frameworks. Simplicity can enhance comprehension, debugging, synthesis, capabilities, and control.

4.2. No-Coding Implementation

In this no-coding approach, the client using our AI solution will not write a single line of code and yet they will transform their unstructured dataset into an algorithm to be implemented. The first step of this process entails a user's current program presenting the AI model with a tabular dataset. The user will set the main outcome of the problem. Following the initial data analysis, the AI model will then generate a report giving more insights by outlining the best-suited algorithms for the data. The client can add or remove any of the data pre-processing operations suggested. After this, a list of suggested model scenarios will be presented, and of all scenarios, the user will choose the best performing, having had the possibility to benchmark them on a test module.

If the user has time constraints or lacks the knowledge to choose based on the problem, the AI model will allow the majority of the solutions to be implemented automatically. Following this process, the best pre-processing pipelines will be reported and, from these, the best-suited algorithm to produce the model, as well as other algorithms suitable for bench testing, will be compiled. Subsequently, the benchmarks can be generated on different algorithms that cater to the client’s objective. After the client has chosen and used the best model from the automatically produced algorithms, they can then save the 'recipe' for it. This will mean that the selected model can be implemented with or without the assistance of the AI model. The user can still observe the behind-the-scenes of all the operations occurring up to the final step.

As opposed to the existing technique for AI development for future AI agent technologies on the user side, the no-coding implementation does not require mandatory knowledge of coding as the development environment, and the user can develop AI through the interaction of explanation requests with AI agents. The idea of development is that any AI novice is given authority by executing and editing AI that was created by another person. In order to arbitrate the correctness of AI, it is necessarily and inevitably a difficult problem, but a user other than the person responsible for the decision believes this AI is a tool for information analysis. This mechanism promotes the acceptance of AI.

Based on a series of discussions about this process, we have compiled three characteristics necessary for the implementation of no-coding technology. There is no mandatory coding. It can be executed locally and is interactable on a desktop or mobile device. There is no network access for AI raw data or intrinsic knowledge. With the idea that it does not involve coding and can be executed in a standalone environment, it addresses the trustworthiness of the AI. Specifically, (1) using the no-coding language, (2) implementing it as a macro, and (3) in order to execute the language processing, using the environment. The tasks that can be imposed on AI agents through implementation using a no-coding framework are either creating information or reasoning and action utilizing existing information. With the research goal of the architecture of future AI agents as "to promote direct interaction", the architectural style being a client/server enabling the AI agent implementation using a thin client is considered.

4.3 Step-by-Step Guide to Building the AI Agent Architecture Using Bolt.new, Lovable AI, and Other Tools

There are other tools created by that can help users further experiment and build the various components of the architecture of an intelligent agent. A suggestion to support the BELD-based intelligent agent is the online information system. Another suggestion is the Business Rule and Inference Specification System, which describes rules, rulesets, triggers, notifications, and inference characteristics for a business in a multi-agent system. In terms of building the system, research shows that the use of computer-aided design can reduce the duration of the design by 30.9% and the time and motion study design by 35.4%. For the purpose of developing hardware game agents, there is an implementation of a simple, circular agent named MiniMax Aliases. The implementation of agents like CMAC and iT Coir-SARSA agents is also provided.

If at any point, while pursuing agent architecture development, one were to use the system and it is not available, can provide specifications and a set of criteria for the rollback of the system from discontinuation. The system will continue to be useful in helping society reduce waste, carbon emissions, and energy consumption. The Project Builder in can be expanded to encompass an Algorithmic System Building Engine, and prospective users will be directed there. If completed, the tool will contain specifications with which the Agent Intelligent Architecture package can be built. The perfect compact and complex application, which would house the inner toolkit utilized for environmental reasoning, feature engineering, and system architecture, would support longer-term success. In this future, the system will provide specifications for everyone and will produce measures of the system's performance. This appendix was included to present a complete guide to the production of in the future, and in this fashion, our zoomed-in approach will enable the agent workhorse of flexing the AI method muscle in specific walks of life. Enjoy!

We expect the previous section may have whetted your imagination about the possibilities of everyday, easy, and joyous AI, which heralds the arrival of the Era of Bestowing AI. This section shows a step-by-step guide to getting to the same visionary position set out in the section and to have the action taken in the AI and robotics fields. Also, we disclose in this section how to get it and install the source code for the data files set out in the previous section. Our step-by-step guide can help scholars, students, policymakers, AI and robotics researchers, engineers, educators, and people who love AI and robotics have an agreed platform for Bestowing AI. We call it "Bolt.new." Thanks to the Lovable AI direction, all the practical AI agent choices available on the Bolt.new site are AI agents for realistic mission days. This guide alone is a selection or creation of AI agents that are intended to complete a realistic mission day with users. Only the Bestowing characteristic graphic abstract of AI agents and their typical areas are intended for day-over-day use for the user community.

Step 1: Design the Input Layer

Intelligent agents are systems that operate autonomously, perceive their environment, and engage in adaptive behavior that achieves specific goals. To enable artificial intelligence agents to make a similarly wide range of inferences about situations and to assist human users, AI agent input layers must be enriched. They must include both exogenous and endogenous variables. First, AI agent input layers must be rich enough to contain sufficiently pertinent facts about a real-world situation. We will argue that current AI agents are limited in terms of their modeling, learning, acting, and interaction environments because their input layers do not usually reflect the totality of information needed by the agent assistants to carefully investigate and draw a wide range of potentially useful inferences about situations. Second, facts about relevant features of human users of AI systems, including aspects of their goals, capabilities, and preferences, are currently in general not part of the input layer considered by AI agents. In this paper, we focus on the components that should underpin a future architecture of AI agents and argue that a deeper understanding of the capabilities of such AI agents is of benefit to society. Although the need for having intelligent agents that interoperate with humans and other software agents has been thoroughly recognized, many technologies that may enable future AI agents have emerged only in recent times. Some of these breakthroughs may fundamentally drive the design decisions behind AI agents. In the near term, it is probably unlikely that specialized agents, tailored to specific tasks, would emerge. In contrast, the design of AI agents that a user can quickly configure to perform tasks of interest is probably more immediate. We argue that for future AI agents to be truly more intelligent and assistive, they should have richer input layers than the ones in existing AI agents.

There are many components of designing an AI agent. I have divided the complete model into a number of steps, and I will be describing each step in sequence. Below are the prerequisites, methodology, schema, and a bit about different layers that I will be describing in the coming sections. I have outlined a high-level framework for the design of the architecture of intelligent agents. With the vision at the doorstep to which the AI is tending, this model is a visionary approach to where this technology is projected.

It seems intuitive to begin with the first layer, the input layer, of the AI system. However, we need to update a few parts of the canonical model of the AI architecture before beginning the design. Most discrepancies are perceived due to the perception of the currently implemented systems that we are commonly aware of; however, here, I aim to present a visionary approach. So, let us begin!

No-Code Tools:

Currently, AI is the buzzword in the tech industry. Everyone is calling themselves an AI company. However, the harsh fact of the matter is that most of the AI programs are pseudo-AI. They are based on the traditional approach and meant for the same. The future of AI lies in creating AI agents with human-like cognitive behavior. The idea of no-code tools is to develop such agents with a framework resembling that of a human's brain to develop such AI agents. During the early stages of developing primitive AI agents, it became apparent that if AI agents had to be trained from scratch for each and every problem, then they would just become advanced algorithms and result in a breakdown of productivity as it would be challenging to optimize and feed in data to be trained. With the increasing number of problems that many AI face, there is a need for an ecosystem of AI agents with associated APIs.

Since the introduction and rise of graphical user interfaces, application developers have been using no-code tools to create simple applications without having to write a single line of code. With the rise of machine learning, no-code tools are the key to the proliferation of machine learning solutions across various domains. Although it is true that as of today, more specialized skills are needed to develop a machine learning solution when compared to developing a simple database application in the past; similar to the case of early programming when simple programming was not as widespread as today, no-code tools will help to reduce the threshold and democratize machine learning. Enterprises and people developing the future architecture of artificial intelligence agents need to have a long-term vision. Calling these agents no-code machine learning tools highlights the short-term goal of automating data science by providing a graphic interface to the process of building and deploying machine learning models. Data science is the first area impacted by no-code tools, but the widespread acceptance of the limitations of AI and the difficulties of creating complete artificial intelligence solutions mean that the industry will need different targeted tools throughout the cycle of AI project development.

Bolt.new: Automate integration of multimodal data like text, images, and videos with its user-friendly platform. Build dynamic user feedback loops and handle real-time data streams through API connectors.

One of the key advances in the new era of artificial intelligence (AI) could be a kind of architectural breakthrough. We propose a future architectural evolution, the Boltzmann-based diverging-aided with-increasing-resolution architecture of intelligent agents. The BoltzDAIR-AI agents can engage in a human-level conversation. In particular, the agents are the first AI systems to acquire a large-scale commonsense semantic model of a natural language sentence. We then apply an array of humanly relevant operations, such as a simulation of human attention and virtual sensory modalities such as images and videos, on this learned representation of the language, resulting in broad capabilities. In the question-answering experiment, the AI agents connect virtual visual scenes depicted by language, challenging humans in the ability to understand and answer associated questions. With capable multimodal reasoning, the agents can handle combinations of text, images, and videos to accomplish multimodal queries. With mechanisms for opportune sharing of semantic information, frequent consulting among agents is possible, supporting further coherent motives and operations in a common environment. We propose a future architectural evolution of intelligent agents, enabling dynamic user data sharing and dynamic changing user access rights. In the new era of the autonomous intelligent agents, the agents scale in capabilities and scope, sparked by a groundbreaking division of labor. Co-located AI agents can be heterogeneously specialized around the improvement of customer or surrounder preferences, for increasing commercial and personal AI-related profits. The agents can exchange simulated and externally detected relevant experience and internal models for anticipatory thinking. This can be applied not only in the car and future transportation industry but in all areas, such as mining. In this work, we extend the government-car architecture toward cooperative working AI agents with various shared responsibilities. We first introduce the concept of the building blocks of the intelligent communications agent and an appropriate service search tree that underpins multinational interactions, and their relation to a user-centered framework. The underlying formalism is discussed and a prototype design architecture based on multiagent system principles is introduced. Finally, we present the prototype AI agent and interface to the government car system, and the results from experimental tests of the presented AI agents in both synthetic and real-world scenarios.

We introduce Bolt.new, the state-of-the-art multimodal AI platform for researchers and engineers, currently sought out by top academics in the field. It provides a user-friendly platform for automating user feedback loops, handling real-time data streams through API connectors, and sharing and reproducing multimodal research. With its user interface equivalent to the Jupyter Notebook, Bolt.new is not only instantly accessible for the entire user base of the Jupyter Notebook, but also allows multimodal functionality usually found through the use of numerous notebooks with scattered and inconsistently managed code. This ease of use complements Bolt.new's highly efficient team-based research, where multiple researchers collaborate on the same project in parallel, cutting down development time and costs. Finally, Bolt.new serves as a bridge for the deployment of multimodal models into applications that are designed to interact directly with real-time data coming from the web. We invite you to join us at Bolt.new and make this multimodal model vision a reality together.

Bolt.new is intended to serve as a bridge for the AI dream, allowing multimodal agents - the future architecture of AI agents - to directly interact with real-time data streams from the social web. The multimodal agents, consisting of image, text, and video modules, will need instant data integration of the different multimodal streams in order to react properly. With its highly demanded functionality, Bolt.new is already invited by academic state-of-the-art multimodal AI research, and can thereby enable the aforementioned AI dreams to become reality. This will serve as our future work, as the classification examples serve to highlight that multiclass predictions for multimodal data are indeed more challenging.

Zapier: Automate workflows for real-time data integration from multiple sources like emails, CRMs, and databases, while enabling feedback loops via integrated forms.

I like to tell a story about the future of artificial intelligence. In my story, Mike and Maria are two AI hopefuls passionate about shaping the future. Despite different backgrounds, the two future AI developers both believe that beyond just augmenting human capabilities via faster computations and improved user experience, AI will actually augment the humans themselves, replacing the necessities of today such as the hunter-gatherer age shortening our daily task orientations. However, today the conceptual frames of AI are built according to existing methodologies and toolboxes, far away from capturing the extraordinary capabilities of augmented humans of the future. Nonetheless the Agent-Human life partnership of the future is anticipated to maximize the welfare metrics of existence. Were there actually any agents involved in the incident in Turkey on June 27, 1992, or the Battle in England on September 20, 1986? How about war crimes? With all the potential ou...

An AI agent could be a chatbot, an intelligent agent on the web, or other software entities that act on data sources—smart devices or emails—and employ automatic machine learning tools. Automated processes search data sources for opportunities and threats and act upon the physical world, generating predictive insights and prescriptive suggestions on a large scale. We present the vision and key characteristics of future AI-agent architecture, assembled from proven data-action model parts. A key part of our vision is utilizing multiple AI agents implemented on specialized computing hardware—a type of network of things operating in a federated learning environment. We describe a near-future use case as a venture finished and deployed for an initial company and market launch, and we present exemplary required technology development to reach operational AI autonomy, demonstrating value creation growth beyond the limit.

A UI agent can be thought of as a more intelligent front end that acts as a gate to machine learning tools with easily available data sources. With user-specific or company-specific machine learning model additions, these AI agents could lead fully automated workflows, such as those typical in financial, marketing, sales, product team, logistics, procurement, or service departments. This paper describes a near-future architecture of AI agents composed by parts of an email automation platform, an integrated AI Chatbot, technology for easy-to-use machine learning model editing, integrated lightning-speed access, and a bot chat gateway—while utilizing computer industry trends to prepare the venture for using technologies, a great human plus machine team model, and low-cost AI supercomputing with a combination of new AI cloud offerings that can provide a low-cost federated learning environment.

Step 2: Develop the Orchestration Layer

The next step in building AI agents that can adapt effectively to human users will be to create an orchestration capability that can manage the sharing of processing and computing resources between agents with different capabilities and users with different tasks, and the alignment and integration of behavior between the agent and user. While the market will help determine a number of the conventions used, new models of performance estimation, new algorithms, and new AI functional elements are necessary to support a free market. These must be centered around how best to enhance the capability of human users working with multiple AI agents and shared data resources. Several areas should be considered in the AI systems processing hierarchy.

Frameworks that provide the abstraction of both user and agent capabilities across a wide range of applications are necessary. These include agents and users participating in functions that can include all common data processing tasks and environments, and anywhere that a user has a human-like presence. Algorithms and models capable of estimating the performance of AI agents in a wide variety of data processing fields, as well as predicting the interaction of AI agents and their human users, are necessary. AI systems must also include AI agents that specialize in executing these functions, and AI agent processing elements that include the basic building blocks of those AI agents' capabilities.

The framework needed for functioning in a shared data environment includes enabling data sharing, adaptive real-time data processing, and maintaining user and agent privacy, integrity, and security. Furthermore, an intelligent orchestration capability is necessary to manage data movement and sharing, and the adaptation and privacy integrity of user and agent interactions. It is also necessary to include AI agents that are capable of providing these basic building blocks for creating and integrating human-like presence. This orchestration capability for the domain provides a shared communication and collaboration channel enabling both fine-grained function-sharing capabilities and interaction capabilities for sharing, and effectively reconfiguring agents to support user objectives. Moreover, it includes an enhanced reward and motivation framework that allocates the compute resources required to achieve objectives, the support provided for each resource, and the interaction-dissatisfaction agent termination capability to support individual user objectives. Highly specialized domain agent elements with task-specific capabilities.

The AI agent will eventually take up many of the responsibilities of technology professionals, which means that a technology professional should be able to design, develop, educate, mentor, orchestrate, utilize, manage, and retire one or more agents. The orchestration layer is the part of this framework that will allow human professionals, machines, and AI agents to define and implement post-cybernetic collaborations. It will provide general and specialized guidance to humans and their intelligent agents on when, for what, and how to effectively collaborate in order to support the objectives of technology.

The framework must have an AI orchestration layer. At a high abstraction level, the orchestration layer can be thought of as a coordinator mediating between and taking commands from multiple executives and implementors. We divide the orchestration layer into four interacting stages. The first stage, Communicate, will allow humans and their agents to explain instructions, request explanations for decisions, iteratively clarify implicit aspects of the instructions and requests, assure that the interactions involving explanations are interpreted correctly, and monitor compliance with ethical and professional rules. The Communicate sub-layer must address up to seven different concerns. The guidance dialogue sub-layer, for example, should allow professionals to provide advice, articulate goals, and develop plans for completion with the agents. This is important because the answer to what should be done must be decomposed into specific tasks that the human or machine implementors of the collaboration can execute. The Explain sub-layer will allow professionals to request, and the agent to provide, the reason, justification, or excuse for a decision or instruction. Here, the request can be made in one of four alternative formats.

No-Code Tools:

No-code tools: democratized AI and architecture design Despite enormous modern advances in AI technologies and tools, most enterprises still struggle to leverage them effectively. As a result, businesses are failing to capture the serious competitive benefits of AI. One of the most significant barriers to realizing the value of these technologies in modern enterprises is a critical lack of technology expertise, with a broad gauge of understanding and potential poor experiences playing key roles in restraining adoption. Among the tools currently available, those requiring less coding skills — the so-called no-code buckets providing a building block, LEGO-like approach for AI workflows — are gaining increasing attention and, as a result, the uptake of more advanced machine learning will be hastened. No-code AI is focused on the simplification of many repetitive and autonomous tasks performed by data engineers or data scientists. It targets liberating the talent so they can focus on key value-add activities such as identifying the appropriate model or feature, automating error correction, and auditing data quality, rather than spending up to 50% of their time on very repetitive and mundane tasks such as data transformation, feature injection, or generation of synthetic examples for models once up and running. Even if some attention was initially focused on the loyal followers, like data scientists or AI enthusiasts, it now seems clear that the broader appeal will initially be developed and dominated by the broader BI/analytics audience or innovation units. In the end, it is important not to forget that democratizing AI cannot be formulaic or flipped with a snap of fingers. A gradient story related to business change and talent adaptation should be pursued, and possibly, top management should invest in a deep learning journey for the organization, creating champions and dreamers in its territories.

These are the tools that will allow generating agents capable of developing tasks without resorting to traditional programming. As an example of this type of tool, an agent is capable of generating agents from the definition of problems. For each problem, it searches for the lower level of organization and presents a modular decision-making agent formed of reactive modules. To generate this agent, a simple template is defined for each module, where the logic, representation elements, and tie-breakers are established. Once these templates are defined, it adapts them to this specific decision-making problem, resulting in a reactive module that can be evaluated and used. Once all the modules that are generated for the different levels of reasoning are generated, they are interrelated through a high level of design criteria. The result is an integrative agent that is specialized with little or no manual development.

To facilitate the handling of the operation and interoperability of these agents, no-machine tools have also been generated, which are called tools. These tools offer a way to generate these interoperable agents in a simple way, simply interacting with the user through the usual forms. These tools are in charge of providing this type of agent with the capacity to operate, select that agreements will be executed, and coordinate operations and unit modifications of the environment in parallel, establishing complete protocols to avoid competitions and deadlock. They are tools focused on developing both the agents themselves and the decision design, similar to reverse engineering any current agent. As with any software, they will initially have limited functionality and therefore will be assigned narrow or maybe several training objectives and will evolve to perform jobs of increasing information intensity.

Lovable AI: Use dynamic task allocation, inter-agent communication, and advanced monitoring tools. Leverage built-in analytics for real-time observability.

We interpret the future architecture of monitoring and observability capabilities as an inevitable trend. We also present ideas on the potential of future AI agents based on our broad exposure to AI technologies across multiple learning domains and our implementation experiences. Embrace the nature of human-driven AI agent collaborations: AI agents will bring years of personal experiences and communication styles unique to any particular person assigned to an area of responsibility through which they gather awareness and expertise. They should make it easy for people to adapt the AI agents' style. Estimating and updating the schedules' likelihood based on observed changes of states, it intends to visualize current, future, and past discrepancies via a range of advanced observability tools. They come alive in hours, integrating diverse capabilities for multi-agent solutions at an architectural level. Use dynamic task allocation, inter-agent communication, and advanced monitoring tools. Leverage built-in analytics for real-time observability. It provides tools for real-time implementation personnel, recognizing their style of work. It should apply all relevant information, ensure the focus on relevant information, and avoid misinterpretation. The expected interactions help us choose those features. Human-driven AI agent collaboration means that humans contributing to and benefiting from given AI agents have different expectations and different relationships. Given the nature of the contributions and benefits, and the active roles of the people behind the different services, designs adopted for monitoring and observability should evolve to meet the design principles established for advanced AI agents.

Make (formerly Integromat): Create complex workflows to automate orchestration, enabling agents to communicate and optimize task performance with ease.

After introducing the visionary future architecture of AI agents, it's all supposed to work out. Well, users typically appreciate when guideline is accompanied by which provides a practical and clear path forward to realizing that visionary approach (unless, of course, they find a path on their own). Here, fine-grained action annotations within a task-specific language model may act as the proverbial Rosetta Stone—facilitating a rich, multimodal communication channel between users and AI agents. Researchers are pushing the boundaries of AI to enable state-of-the-art models to perform important tasks written in natural language. Agents create complex workflows that automate the orchestration of other data-centric, specialized AI agents. Non-experts create digital assistants who combine local expertise with an ability to readily engage and orchestrate with online agents holding a vast amount of global knowledge. The research team proposes fine-grained action annotations, known as micro-actions, in natural language for controlling a task-oriented AI model.

Make: Create complex workflows to automate orchestration, enabling agents to communicate and optimize task performance with ease. Streamline the orchestration of your task integration workflow via Make, the company’s newest API orchestration engine. Make's reusable bots provide templates like default flows and data transformations to optimize your workflow. Performance of tasks is by default directed towards a specific goal. Organizing the performance of the task is referred to as orchestration. Orchestration’s efficiency should avoid the traps of traditional narrowing and be future-proofed against the effects of automation adoption. From the bodies of related work and the case study, our research question formulated was: In the context of task design by peopling AI agents, how can we define AI’s relationships and potential roles to minimize hindering orchestration in, and as a result of, task performance?

Step 3: Define AI Agent Core Capabilities

1. The following are the very core capabilities we can assume of any AI agent. These are context-independent:

• Reasoning, where the AI agent generates new information and understanding based on a set of input data. • Sensemaking by an AI agent, which includes analyzing multiple information inputs to interface between reality and the AI agent’s current knowledge or goals. • Knowledge/action, which includes the knowledge capability of an AI agent to store and use information in both declarative and procedural form, and to act by following goal-oriented rules or algorithms. • Locomotion, which is the capability for AI agents to move on their own or be self-consistent to make changes to software services that others can access when the software is performing. • Turbulation, which is the ability of an AI agent to analyze several aspects of issues and to discern and make decisions when there is an issue. • Deception; although most would think that deception should never occur, it should be noted that the capability for deception needs to be specifically coded out of an AI agent, with the potential for being included in the AI agent's architectural design for military purposes. • Powering capability, including rest, self-reasoning, and improvement processes coordinated with power fluctuations or sudden outages. Most agents work by starting to process data as their power is ramped up. Only with new levels of power will the AI agent have, and go into standby state, preserving the state and waiting for a power reboot.

2. The function calls of an AI agent present a similar architecture to other components of a system and warrant inclusion in their AI agent profile documentation. These AI agent function call groups break into three categories: • General functions (the AI core capabilities) • Mind • Reporting.

HLMs (thinking scenarios) cover a wide range of events and contexts that AI members may become involved in. Every AI architecture and framework has an implicit or explicit model of the AI that is created in the AI member's platform. Reaching a consensus model starts a collaborative project dedicated to the design and assessment of the AI process design. Member importance in the design process will depend on the intended use and the need for member oversight in this process. The outcomes of this proposed AI core capability could become the tentative guiding principles for defining different thinking scenarios and real-world thinking processes.

Modeling the AI agent architecture focuses on the process and capabilities in these thinking scenarios and multi-stakeholder interactions and oversight options in these processes. It attempts to demystify the AI design process. Thus, the manifestations of the AI's design architecture cover three broad domains: all design-related issues and vulnerabilities of an AI's behavior and capabilities. In these AI agents' design domains, we propose that these major design categories be explored and addressed via a multi-stakeholder process in order of importance for focus and attention.

No-Code Tools:

No-Code Tools

As we're making steps towards code-free AI models, it's also great to think about how far no-code tools will go! One of the projects that constantly amazes is a tool dedicated to making deep learning-related algorithmic work more accessible, expressive, and useful for creators and to help improve the literacy and understanding of industry best practices in artificial intelligence-powered creative work. Some of the already solved 'no-code' tasks include data preparation and feature exploration. The future direction is model deployment and automatic interpretation of deep learning models working with different data types. It's going to be very interesting to see the next step and what great intuitive creators will come up with.

Another tool shares the same vision: to make image segmentation tasks simple and intuitive for everyone—not just data scientists. It's time to bring these outdated, complex deep learning techniques into the future and make it so simple a dog could do it…and I think he can and will.

Another magical no-code tool is dedicated to natural language processing. Used already by a significant portion of the Fortune 500! This tool helps people and their teams work together with superior language AI. Their NLP-focused chat helps get things running with no coding required! Interactive writing experience for everyone. Built-in version control, fast code review, and project insights.

Furthermore, the use of specific and free tools would be employed by AI agents to solve individual AI functions that would be offered via the cloud. The owner of the AI agent would be able to request any AI service expansion through the purchase of the desired service or through a contest in which the best AI agent would be awarded. These AI agents could also have the capability to improve their performance and services by connecting to the vendors of such tools. These tools would be in part subjected to a dual system of licenses to promote their use and access to them. The end goal is to be able to achieve the most complex activities of a software factory, from building software to the need to maintain it. This should ensure that these entities not only have the ability to solve increasingly professional or utility problems, but also promote employment and territorial presence. All this in anticipation of the arrival of artificial brains.

Bolt.new: Build workflows for strategic decision-making and continuous improvement loops. Integrate various AI tools and pre-trained models for intelligent tool utilization and learning.

The rapid growth of AI technologies in recent years has made it clear that AI agents will play an increasingly important role in the future, especially in strategic decision-making or strategic management of organizations. In this paper, we articulate an architectural proposal that takes into account the business aspects, as well as the AI tools that may be used in this context. In addition, we show how this architecture can be instantiated in different business scenarios - ranging from planning to analytics - and integrated with state-of-the-art pre-trained models. We argue that this kind of architecture allows not only the implementation of intelligent tools, capable of taking strategic decisions on their own, but also continuous improvement loops in their design, often with the participation of decision-makers who are not AI experts. The results of this research may support the business community in understanding some of the advances in this interdisciplinary field that involves strategic decision-making.

Data-driven decision-making and strategic management in organizations tend to remove some common qualitative management and strategic planning activities. This is because having access to data and their spatialization using business analytics techniques allows the visualization of problems, the analysis of operational historical series, and the continuous monitoring of results. Also, it is possible to create predictive models that allow for a criminal or customer retention model, for example. However, although many business decisions can be made by data-driven decision-making techniques, strategic decisions are usually more complex and often involve varying degrees of uncertainty in their imposition.

Workflowception, the dream within a dream for strategic decision-making and continuous improvement: This describes the future architecture of AI agents. When people use AI tools, they often don't do workflow design and execution with AI, which involves calling, extracting, and utilizing intermediate AI tools together. However, as the number of basic AI tools is increasing, the ability to design and analyze the utilization of intermediate AI tools linked in workflows is important. Boring and repetitive workflows can be automated first, and workflow execution can be improved through the utilization of intermediate AI tools. Then, AI can directly replace experts in strategic decision-making.

We describe an AI agent capable of calling and constructing new AI agent workflows consisting of linked intermediate AI tools, Argumentation agents capable of constructing team-building workflows connected with a single workflow by generating arguments for strategic decision-making, and a Pre-Training agent mimicking the main AI tool. Detranslation is an agent forming a team by training humans with an argumentative method, respectively. In the future, more than half of the AI users will use AI for strategic decision-making and agent creation in addition to repetitive task automation. I will argue that it is a good idea.

Bubble: Develop interactive applications for AI decision-making and reflection mechanisms with a no-code visual interface. Use plugins for additional AI functionality.

Our mission is to enable AI to be beneficial and equitable for society. We use multi-objective techniques to enforce ethical requirements for performance and fairness. We use our advanced ethical AI decision-making engines to make automated decisions that improve societal welfare. In a vision of the future architecture of AI agents, the results of this work are consumed by AI/ML model applications as input data to generate beneficial decisions. However, model applications cannot frame problems, reflect on alternative resource distributions, or directly consume abstract concepts like options and moral values. Therefore, we have aligned Bubble functionality to our clients, who integrate AI decision-making engines into use cases for natural interaction with moderate-sized decision permits.

The results of this work are consumed by AI/ML model applications as input data that generate beneficial decisions. However, a model cannot frame a problem, reflect on alternative resource distributions, or directly consume abstract concepts like options or moral values. To address these needs, we have created Bubble: a suite of both AI/ML decision-making engines executed as function calls in a high-level programming language to formulate expressive, ethical AI/ML problem specifications with a companion visual API design product constructed with a user-friendly interface that facilitates creative interaction using a plug-and-play business logic to develop interactive applications for AI decision-making with no training in artificial intelligence programming required. Then, Bubble converts the results of user interactions or metadata into formal inputs to AI/ML decision-making engines and scrutinizes the results using reusable reputation-computation mechanisms and reflective questions before suggesting them for client deployment.

The Zero Bubble brings language model AI decisions and reflections with positional multipolar architectures that break concrete goals into the most promising paths towards achieving these goals from the model down to the user interface. It's a powerful foresight reflection and decision executive tool for people to play the well-known reinforcement learning psychological experiment, which constructs humans as agent models and allows for quantitative player participation by quantizing model updates. We gain insights into human decision incongruence due to biases caused by the structures of human reflection, which have been left out in traditional experimental reinforcement learning. Furthermore, we demonstrate how to train aligned environments that reflect the notion of problematic durability of AI systems. The Zero Bubble also bridges prior human/AI language model-datatype differences and information value differentials between the human and the AI language model.

The architecture is made dynamic through programmable agreement between the developers' decision classes and a crowdsourced community of instantiations that modify the respective public model reflection response mechanics, implement decision responses, or provide the initial data refresh for developers to reduce abuse in complex decision contexts. Development time is reduced to zero when agreement is successful. Decisions of context-specific models in no-code visual decision interfaces enforce alignment of reflection of the decision models across multiple levels in the Zero Bubble AI Decision Tree. We use decision-making primitives superimposed only with a modified dialog operator to create decision advantage in human-AI cooperation prior to formal proof of its logically correct implementation.

Step 4: Implement Data Storage and Retrieval

For the 'PlainDatastore' datastore, we store both keys and serialized objects. The serialized objects provide detailed user behavior history for predicting potential links not restricted to certain users, while there is no restriction on the total number of slices. The key list provides a quick access method to locate the relevant data slices for generating the training data. The operative phase adopts a lazy evaluation method to speed up data applicability in inference services based on the reinforcement learning environment. Specifically, when the environment is initialized, the service data slices are deserialized and turned into space in memory, while the additional activation functions of each slice are loaded into the service corresponding to user and item ID records of the function tuples. The partition structure also has a function list map, thus the environment/agent can access function pointers corresponding to the current state vector efficiently. Its drawback is that most of the initial overhead will be spent running large-scale traffic programs.

The 'FieldDatastore' datastore is associated with the proposed field slicing, where every single field can cut attribute spaces into several continuous slices for each user and record the detailed item usage behavior in the corresponding data slices in the feature engineering stage. For the training data generation in offline cache, after users and items are randomly accumulated in exploration scenarios, the joint distribution of all possible feature combination patterns is accurately described, and function evaluation can get the features of all available items for all users. When the data is transformed into the serving units for the inference service, and such computing the feature part is a high-frequency image processing scenario, this dataset will provide enormous training data storage and can be maintained in offline trainers. When we need training data, the record in the lifecycle of the cargo is read, and fields such as the map with all the access list indexes in the storage lifecycle and feature information are aggregated and joined. The performance is high since there is a restriction on the number of slices, and only that. However, its application scenarios are fewer than the text synopsis considering the overhead to design a custom data storage format with the proposed data status.

The architecture of the AI agent includes data storage and retrieval in addition to knowledge extraction and its function. Storage includes several critical datasets. The first dataset is the knowledge article database that contains various knowledge topics. It is the same as the topic database, which contains cross-references between various topics. Each article has attributes that include the answers to the top queries associated with it. These answers can be adequate for a user query or need revision based on user feedback. The second type of dataset is the expert response database, which captures the preferred expert response with reasoning for each known knowledge topic. The knowledge retrieval-focused function domain module must parse and traverse the topics using a point-of-view paradigm. It should enable queries from multiple interrogative prompts like how, who, when, what if, and why. The model has the flexibility to use binary, trinary, or higher order reasoning methodologies while handling questions from the user.

The point of view or perspective of the user query could also be used to guide the ranking of answers from the response dataset. Top answers with high rank could be returned to satisfy the user query. Further, deep learning-powered natural language model textures could enable the customization of responses in relation to user queries. Providing just the expert response as the definite answer may not be useful for the user. AI solution providers have to plan for various levels of vagueness in user queries and appropriately enrich the natural language understanding capabilities of AI programs.

No-Code Tools:

We can expect a future of ultra-smart AI agents, capable of providing a wide range, depth, and quality of value-adding services that no programmer can anticipate or effectively specify. Some of these agents will have integrated, complementary abilities to form agent teams. We foresee that these agents will be used by typical business users, in addition to those currently involved in using and developing AI, and will be used in various domains on a vast scale. A significant advance in the architecture of AI agents and agent teams is needed to evolve the current AI capabilities into the promised scenario of future AI. That is, with future no-code tools, end users, not just AI developers, should be able to connect, use, and modify AI capabilities, using satisfaction-oriented graphical interfaces and interaction paradigms, and then let the resulting AI agent team learn incrementally to interpret when it should be triggered and to behave accordingly, and perform its tasks fluently.

We wish to lay out a roadmap for the no-code future development of AI agents – a toolkit that will help business users express their communication needs to the platform through various multimodal inclinations and activities. The platform will then take care of the training and communication aspects, toiling with the related communication data in its native form. For some selected no-code techniques, a concrete state-of-the-art specification will be reviewed and a discussion will outline their future challenges and promising research avenues. Each of these techniques has shown high potential to promote the democratization of multimodal AI, and the engagement and social dimension of networked platforms nowadays. With this overview, we have aimed to offer insight for enabling business users to start and participate in a discussion on multimodal AI with some immediacy, without having to wait for an extended period of time before this topic starts to affect their everyday work.

No-code methods for visualization are critical for enabling people to better understand the data produced by machine learning and AI algorithms. We envision a new generation of no-code visualization tools that will enable anyone to interactively and iteratively explore AI data, models, and results. These tools will have several features to ensure that people can assess the capabilities, processes, and outcomes of AI and ML algorithms. One critical feature will be guidance, presenting users with prompts for inquiring about the data produced by AI algorithms and supporting them in iteratively posing questions and interpreting the responses.

Another key challenge that no-code AI visualization tools will need to address is supporting collaboration among people with diverse and asymmetrical expertise. In general, the building of richer representations and algorithms should be a collaborative process that engages people with a wide range of skills, including domain experts, machine learning and machine perception experts, and human factors and user experience experts. The next generation of no-code tools for visualization should reflect and enable this kind of collaborative work and thus better support powerful, fair, transparent AI systems. We provide a visionary conceptual roadmap for these no-code tools, drawing on and synthesizing relevant work from human-computer interaction, visualization, databases, human-centered AI, and machine learning, as well as current work directly addressing the visual representation of data produced by AI and ML models.

Bolt.new: Manage structured and unstructured data with flexible storage solutions. Integrate vector databases and knowledge graphs for efficient data retrieval.

The successful integration of AI agents into our daily life deeply depends on the design of their architecture. The need of the present and the future is to transcend the boundaries of traditional AI applications, and produce intelligent systems capable of dealing with real world problems. In this context, we introduce a multi-phased architecture that may be applicable in the future. In this respect the agents would be capable of offering high-level interactions related to decision-taking, based on their ability to accommodate complex, multimodal and multisource information, reason logically, plan ahead for time-related events, act reasonably, and welcome the capacity for self-repair mechanisms. The aim is to address these characteristic properties of such an agent. The idea opens new interesting perspectives and challenges for further AI research.

It is the premise of this study that it is necessary and promising to start with a good theory capable of accommodating inferences about the complex and incomplete nature of real-world information, and design an architecture compatible with that theoretical model. Due to the need to democratize AI technology, and expand its citizen-centered application prospects, artificial intelligence must be open to everyone. All the components herein presented can content a broad scope of AI users including data scientists, machine learning practitioners, software developers, analysts, business and public policy decision-makers and stakeholders.

The debate on the future architecture of AI agents has led to a speculative approach and often involves the vision of 'human-like thinking' in AI systems. In this opinion, this paradigm can be counterproductive in framing the future development goals and stakeholder integration for the design of AI agents. Instead, a vision is presented for an AI architecture similar to the existing human-based knowledge economy, where the functioning of human knowledge is not only supported by our human intelligence-based functions but also grants a pivotal role in the access and development of an individually unique knowledge database. The work focuses on intelligent AI agents evolving to acquire a similar architecture that allows structured and unstructured data management and retrieval for the purpose of empowering big data processing and decision-making intelligence.

The data platform introduces multiple innovations empowering the efficient management of structured and unstructured data. The core computational database engine – the CloudPredict database – recently outperformed large cloud players in in-memory storage and compute-based performance measurements with a single instance. The seamless integration of computational and data storage engines is designed to support AI and machine learning serving with any storage workload and morphology, the pipeline of complex, multi-structured datasets for accelerated storage-based analysis and integrates vector databases and knowledge graphs for efficient data retrieval.

Airtable: Organize data into structured databases with visual layouts. Combine with advanced tools like Pinecone for vector-based retrieval or Neo4j AuraDB for managing knowledge graphs.

We present a possible architecture for AI agents at the level of broad principles. This architecture is based on a set of extensive experiments with present-day AI systems, so it is not science fiction. We describe how we have incorporated this vision in a present-day working intervention system and the broader capabilities and research strategy this entails. Our aim is ambitious. We seek ways to develop and guide AI systems that have a richer internal economy than present-