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Autonomous AI Agents: A Step Closer to Independence

The development of Artificial Intelligence (AI) is progressing rapidly. A particularly exciting field is the development of AI agents that can independently perform tasks and interact with their environment. Traditional approaches, such as those used in ReAct-based workflows, rely on external input to control interaction with tools and the environment. This significantly limits the autonomy of the models and requires constant human intervention.

A new approach, which has the potential to overcome these limitations, focuses on the development of so-called Large Agent Models (LAMs). These models internalize the generation of chains of action (CoA) and enable the AI to independently decide when and how to use external tools. Instead of waiting for external instructions, the agent proactively plans its actions and executes them autonomously.

A promising framework for developing such LAMs is AutoCoA. This approach combines supervised learning (Supervised Fine-Tuning, SFT) with reinforcement learning (RL). This allows the model to seamlessly switch between thought processes and actions while efficiently managing interaction with the environment. AutoCoA consists of three main components:

The first component is the stepwise activation of actions. At each step in the course of action, the model independently decides whether and which action should be performed. This allows for flexible adaptation to dynamic environments and unforeseen events.

The second component is the optimization of the entire chain of action. Instead of considering individual actions in isolation, AutoCoA evaluates and optimizes the entire sequence of actions to achieve the best possible result. This enables long-term planning and strategic behavior.

The third component is an internal world model. This model simulates the effects of actions in a virtual environment before they are executed in the real world. This reduces the cost of interacting with the real environment while accelerating learning.

Initial evaluations of AutoCoA in open-domain QA tasks show promising results. The agents trained with AutoCoA significantly outperform ReAct-based workflows, especially in tasks that require long-term thinking and multi-step actions. This suggests that internalizing the chain-of-action generation is an important step towards autonomous AI agents.

For Mindverse, a German company specializing in the development of AI solutions, these advances in agent modeling offer exciting possibilities. From chatbots and voicebots to AI search engines and customized knowledge databases, the ability of AI agents to act and learn autonomously opens up new avenues for the development of innovative and powerful AI applications.

The development of autonomous AI agents is still in its early stages, but the potential is enormous. With frameworks like AutoCoA, we are taking a big step closer to the goal of developing truly independent AI systems.

Bibliography: - https://huggingface.co/papers/2503.06580 - https://huggingface.co/papers - https://arxiv.org/pdf/2502.02508 - https://arxiv.org/pdf/2502.01562 - https://www.researchgate.net/publication/229083859_Methods_for_Model-Based_Reasoning_within_Agent-Based_Ambient_Intelligence_Applications - https://spcl.inf.ethz.ch/Publications/.pdf/besta-reasoning.pdf - https://www.researchgate.net/publication/389593553_Human-centered_World_Modeling_Enhancing_Multi-Agent_AI_Adaptability_with_Chain-of-Thought_and_Symbolic_Reasoning - https://github.com/open-thought/system-2-research - https://medium.com/@jakebatsuuri/analyzing-react-synergizing-reasoning-and-acting-in-language-models-paper-3ddb963e3bd6 - https://www.linkedin.com/posts/martin-ciupa-76418b17_the-evolution-of-ai-agents-agentic-systems-activity-7273731703636987904-ucpL ```