Emergence AI

Welcome to Emergence, where the future of enterprise workflow automation begins. Listen to exciting words from our co-founders Satya Nitta, Sharad Sundararajan, and Ravi Kokku, Learn Capital's founder and investor Rob Hutter, our research scientist Ashish Jagmohan, and our Chief Design Officer Hélène Alonso as they share how we’re advancing the science and development of #AIagents. Follow us to discover how intelligent agents will unlock the full potential of #AI in enterprise systems.

Discover how AI agents can adapt to dynamic and challenging enterprise environments👇 In our recent Live Q&A, our Research Scientist and Manager, Aditya Vempaty, joined our VP of AI Agents, Vivek Haldar, and VP of Developer Relations and Community, Waqas Makhdum, to discuss the launch of our enterprise-grade multi-agent orchestrator and its capabilities. Diving into how our multi-agent orchestrator balances the need for adaptability, determinism, and reliability, Aditya Vempaty shares: ✔️ ⁠How our orchestrator flexibly adjusts to complex enterprise environments, accurately assigning AI agents to specific tasks. ✔️ How the orchestrator strikes a balance between adaptability and determinism, focusing on reliable output. ✔️ How self-improvement enables agents to add new skills and align with evolving enterprise systems for long-term success. Explore real-life use cases with our multi-agent orchestrator and drive productivity gains for your enterprise 🚀 #AIOrchestration #MultiAgentSystems #EmergenceAI

Delighted to share a subset of E-Web: a new Web benchmark that our team at Emergence AI designed to rigorously evaluate Web agents. E-Web aims to set a new standard for assessing AI capabilities in real-world enterprise settings. We are releasing many prompts in the open source, and a paper that describes the methodology in developing the benchmark. Benchmark Paper: https://lnkd.in/e5fgFs4R Benchmark Github: https://lnkd.in/eNsXwk_8 What Sets E-Web Apart? - Skill-centric Design: Core, transferable skills such as form filling, dropdown handling, and file operations that are common across applications. - Real-World Relevance: Enterprise tasks based on tools like Salesforce, JIRA, and LinkedIn. In the evolving landscape of AI, there is a clear need for benchmarks that reflect enterprise use cases. While E-Web is a step forward, we believe that there is much more to explore. We look forward to seeing contributions that extend and enrich this benchmark. Heads-up: Multiple Web agents do poorly on these prompts. We will share the evaluation results soon. #WebAgents #EnterpriseAI #Benchmarks #AgentE #Agents

Thanks to Agency for featuring Emergence AI in their 2024 AI Agent Market Map! We recently launched our enterprise-grade multi-agent orchestrator — an autonomous meta-agent that can plan, execute, verify, and iterate in real time. It combines human-like interaction and navigation with machine-level scalability, enabling businesses to orchestrate operations across web front ends, APIs, and both modern and legacy enterprise systems, unlocking valuable use cases. Keep an eye out to see how we advance multi-agent orchestration in 2025 🚀 #EnterpriseAutomation #AIOrchestration #WebAutomation #EmergenceAI

🔎 𝐄𝐦𝐞𝐫𝐠𝐞𝐧𝐜𝐞 𝐑𝐞𝐟𝐥𝐞𝐜𝐭𝐢𝐨𝐧𝐬 𝐒𝐞𝐫𝐢𝐞𝐬 | #5 𝐇𝐨𝐰 𝐝𝐨𝐞𝐬 𝐨𝐧𝐞 𝐮𝐬𝐞 𝐦𝐮𝐥𝐭𝐢-𝐚𝐠𝐞𝐧𝐭 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐦𝐢𝐬𝐬𝐢𝐨𝐧-𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥, 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐬𝐲𝐬𝐭𝐞𝐦𝐬? * 𝐄𝐱𝐩𝐥𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐯𝐬 𝐄𝐱𝐩𝐥𝐨𝐢𝐭𝐚𝐭𝐢𝐨𝐧: Agentic systems with their inherent stochasticity can be very useful to explore new design spaces for multi-agent orchestration to transform enterprise workflows. Once the design is validated by target performance metrics or humans, it can be reliably exploited again and again in operations time. * 𝐑𝐞𝐜𝐨𝐯𝐞𝐫𝐲 𝐟𝐫𝐨𝐦 𝐞𝐫𝐫𝐨𝐫 𝐦𝐨𝐝𝐞𝐬: While a validated mult-agent orchestrator design can be exploited repeatedly for a deterministic and predictable operation, which is important for most enterprise workflows, it still does not fulfill the vision of a “reliable” agentic system. When an error mode is encountered, the orchestrator should be able to quickly re-plan and reconfigure the system to ensure that normal operations of the system can be quickly restored. * 𝐇𝐮𝐦𝐚𝐧-𝐢𝐧-𝐭𝐡𝐞-𝐥𝐨𝐨𝐩 𝐢𝐬 𝐭𝐡𝐞 𝐤𝐞𝐲: As agentic systems dynamically reconfigure themselves to recover from errors, it is extremely critical that there is a well-defined Role-based Access Control (RBAC) for agents (as much as humans) while deploying multi-agent orchestration systems in the enterprise. Based on the criticality of the change operation (Read, Write, Modify, Delete), the orchestrator should be able to approve them without human intervention or fall back on humans for approval when any change can: - Have a significant impact on the core process - Result in regulatory compliance issues - Cause integrity issues to the core data for the process - High financial impact * 𝐒𝐞𝐥𝐟-𝐢𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭: As the system collects more and more approvals/rejections from humans during operations and recovery from errors/outages, it learns from these experiences and falls back lesser and lesser on humans over time. This ensures a graceful evolution of the system towards limited autonomy without compromising on reliability, which is so important in enterprise operations. #EmergenceAI #AgentsInEnterprise #SelfImprovingAgents

🔎 𝐄𝐦𝐞𝐫𝐠𝐞𝐧𝐜𝐞 𝐑𝐞𝐟𝐥𝐞𝐜𝐭𝐢𝐨𝐧𝐬 𝐒𝐞𝐫𝐢𝐞𝐬 | #4 𝐖𝐡𝐲 𝐢𝐬 𝐩𝐥𝐚𝐧𝐧𝐢𝐧𝐠 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐢𝐧 𝐦𝐮𝐥𝐭𝐢-𝐚𝐠𝐞𝐧𝐭 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬? Planning and executing complex workflows in enterprise settings such as #compliance, #QA, #research, and #ProjectManagement comes with unique challenges: balancing reliability, cost-efficiency, flexibility, and robustness to unexpected inputs and results. Our orchestrator platform addresses these challenges with innovative solutions: * 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠: Breaks down complex intents into discrete steps, dynamically generating and executing plan steps to ensure resilience against error and allow replanning where necessary. * 𝐑𝐞𝐮𝐬𝐚𝐛𝐥𝐞 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬: Stores successfully executed plans for retrieval, boosting consistency, reducing latency and cost, and allowing for parallel execution of steps. * 𝐑𝐨𝐛𝐮𝐬𝐭 𝐕𝐞𝐫𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Ensures quality with final-output checks, step-level verification, and human-in-the-loop oversight when needed. Learn more about how we’re streamlining planning and multi-agent collaboration for smarter, faster, and more reliable outcomes for #MultiAgentOrchestration in enterprise workflows: https://lnkd.in/egr6NJae

🔎 𝐄𝐦𝐞𝐫𝐠𝐞𝐧𝐜𝐞 𝐑𝐞𝐟𝐥𝐞𝐜𝐭𝐢𝐨𝐧𝐬 𝐒𝐞𝐫𝐢𝐞𝐬 | #3 𝐇𝐨𝐰 𝐜𝐚𝐧 𝐰𝐞 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐝𝐞𝐬𝐢𝐠𝐧𝐞𝐝 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐰𝐞𝐛 𝐢𝐧 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐞𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐬 𝐦𝐨𝐫𝐞 𝐞𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞𝐥𝐲? The web is a dynamic and multifaceted landscape, and when applied to enterprise scenarios, the challenges for AI agents become even more complex. With E-Web, we introduce a new benchmark for assessing how AI agents navigate and execute real-world web tasks that are tailored to meet the rigorous demands of enterprise applications. Dive into the details here: https://lnkd.in/e5RA7jPn #AIResearch #AIAgents #EmergenceAI

🔎 𝐄𝐦𝐞𝐫𝐠𝐞𝐧𝐜𝐞 𝐑𝐞𝐟𝐥𝐞𝐜𝐭𝐢𝐨𝐧𝐬 𝐒𝐞𝐫𝐢𝐞𝐬 | #2 𝐇𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐦𝐞𝐚𝐬𝐮𝐫𝐞 𝐩𝐫𝐨𝐠𝐫𝐞𝐬𝐬 𝐰𝐡𝐞𝐧 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬? In our latest whitepaper, we explore the critical role of benchmarking in accelerating AI agent adoption within enterprise settings. From reproducibility to bias and real-world applicability, we highlight key challenges of evaluating AI agents and present actionable strategies for designing scalable, enterprise-ready benchmarks. Discover a perspective designed to help teams align their goals, enhance decision-making, and drive meaningful advancements in AI. Read more here: https://lnkd.in/e-hUqVPX #AIResearch #AIAgents #EmergenceAI

🔎 𝐄𝐦𝐞𝐫𝐠𝐞𝐧𝐜𝐞 𝐑𝐞𝐟𝐥𝐞𝐜𝐭𝐢𝐨𝐧𝐬 𝐒𝐞𝐫𝐢𝐞𝐬 | #1 𝐇𝐨𝐰 𝐰𝐢𝐥𝐥 𝐝𝐢𝐬𝐩𝐚𝐫𝐚𝐭𝐞 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐜𝐨𝐧𝐧𝐞𝐜𝐭 𝐭𝐨 𝐞𝐚𝐜𝐡 𝐨𝐭𝐡𝐞𝐫 𝐢𝐧 𝐧𝐞𝐰 𝐚𝐠𝐞𝐧𝐭𝐢𝐜 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬? As we started working on developing multi-agent systems that work with legacy enterprise systems, we quickly realized that to make this work, we needed to combine a web agent (for web systems) with a structured connector agent (to link existing APIs and databases). Building a robust connector agent has been a core focus for our team, but it’s not as simple as it sounds. Here’s why: * 𝐀𝐏𝐈 𝐃𝐢𝐬𝐩𝐚𝐫𝐢𝐭𝐲: Enterprises use a mix of SOAP and REST APIs, each with its own quirks. Handling them correctly is critical. * 𝐋𝐚𝐜𝐤 𝐨𝐟 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: Many enterprise APIs don’t have comprehensive documentation, so exploring their usage often requires technical trial and error. * 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: There are few benchmarks tailored to enterprise APIs, making it tough to build a reliable evaluation framework for connecting systems efficiently. As discussed in our SEAL paper, any evaluation of such a system has to be multi-level in order to be comprehensive. It should be evaluated on retrieval of the right APIs, selection of the right parameters, and, of course, the final response generated. Despite these hurdles, we’ve made great progress! In 2025, we’re launching exciting new features for our API connector agent, which will be a game-changer for our orchestrator. We’re thrilled about what’s coming and can’t wait to share it with you. As 2024 wraps up, our team is taking a moment to reflect on the year’s journey—highlighting the challenges we’ve faced, the victories we’ve celebrated, and what’s coming next. Join us in this series as we share insights into our team’s growth, what we learned, and our exciting plans for 2025. Stay tuned! #AIAutomation #MultiAgentSystems #EmergenceAI

Thank you CB Insights for listing Emergence AI in your 2025 Tech Trends Report under “Multi-agent and Orchestration”. We recently unveiled our enterprise-grade multi-agent orchestrator, which is capable of planning, executing, verifying, and iterating in real time. By combining design-time flexibility with run-time orchestration and integrating our API Agent and Web Agent, our orchestrator helps businesses transform their workflows and increase productivity at scale without sacrificing data privacy and control. This is just the beginning — stay tuned as we push the boundaries of what multi-agent orchestration can do for enterprises! 🚀 #MultiAgentSystems #EnterpriseAI #EmergenceAI