📊aelf Ventures Spotlight: How AI Agents Achieve Goals🤖 #AIagents are essentially large language models (#LLMs). Traditional LLMs generate responses rooted in the data they were trained on and are bounded by knowledge and reasoning constraints. Today, sophisticated agentic technology is able to leverage backend tool integration to access real-time information, streamline workflows and automate tasks to achieve complex goals effectively. AI agents can now adapt to user expectations by learning from past interactions and planning future actions to provide comprehensive and personalised responses, even without human intervention. Achieving user goals involves 3️⃣ key stages: 🔵Goal Initialisation and Planning🌟 While AI agents can operate autonomously in decision-making, they still rely on human-defined goals, tools and environments. Given the user’s objectives and available resources, these agents generate a series of tasks and subtasks, refining their outputs at each step to achieve complex goals efficiently. 🔵Resources for Agent Reasoning📚 AI agents make decisions based on perceived information, yet often lack the complete knowledge and resources needed to accomplish complex tasks. To bridge this gap, they utilise tools like external datasets, web searches, APIs and other agents to fill in missing information and update their knowledge base, enabling continuous reassessment and self-correction during task execution. This interaction between tools allows AI agents to tackle a wider range of tasks compared to traditional AI models, providing more general-purpose assistance. 🔵Reinforcement Learning🧠 AI agents improve the accuracy of their responses through feedback from other AI agents and human-in-the-loop (HITL) input. For instance, after presenting a prediction to the user, the agent saves the learned information and user feedback to refine future performance, adapting to user preferences. Feedback from multiple agents minimises the need for user direction, while feedback from users ensures better alignment of outcomes with intended goals. This iterative refinement process enhances the agent’s reasoning and accuracy, while storing past solutions in a knowledge base helps to avoid repeated mistakes. 💻Future of the AI World AI agents marks a major step towards the agentic web. By integrating with tools and real-time resources while learning from feedback, they can dynamically reassess and adapt their strategies to navigate complex goals with higher precision and personalisation. They are set to transform the digital assistant landscape, offering innovative solutions across various domains and enhancing productivity in unprecedented ways. 💡You can also read learn how AI agents learn, adapt, and complete tasks on our blog: https://lnkd.in/gdmwypyh
