Autonmous Agent

In this article, we will explore the capabilities of large language models (LLMs) beyond simple chatbots. We will delve into the concept of autonomous agents and concentrate on constructing an autonomous agent capable of managing complex, real-world tasks.

Full code samples are available on GitHub.

What is an autonomous agent?

An autonmous agent is a program or system that is designed to act independently and make decisions without direct human intervention. Unlike a simple chatbot, an autonomous agent can perform complex tasks, such as managing a company's supply chain, analyzing large datasets, or even developing software.

An autonmous agent uses LLM functions as the brain and integrates with several key components: planning, memory, and tool use.

Planning

Planning is the process of determining the sequence of actions that will achieve a goal. In the context of an autonomous agent, planning involves subgoal decomposition and action refinement.

Memory

Memory enables an autonomous agent to retain and recall (infinite) information over extended periods. Usually, the memory is implemented as a external vector store.

Tool use

Tool use is the ability to call external API for extra information that is missing from the agent's current knowledge, including current information, code execution capabilities, and more.

Which tasks are suitable for autonomous agents?

In practice, autonomous agents excel at handling tasks that are repetitive and demand significant manual effort. For instance, a company can leverage an autonomous agent to efficiently manage its supply chain, analyze extensive datasets, and even assist in software development.

Milestone

MetaGPT (Hong et al.) first proposed a multi-agent collaboration framework to solve complex tasks.

AutoGen (Wu et al.) extends xx

RD-Agent (Yang et al.) studies xxx

DSPy (Omar et al.) proposes a xxx

Challenges and disscussions

• Agent communication overhead
• Agent management

Hands-on examples with AutoGen

Expert-level assistant

Conclusion

xxx

References

1. Lilian Weng. "LLM Powered Autonomous Agents" Blog (2023).
2. Amit-Lal. "Uncover the Future: Microsoft Autonomous AI Agents analyzing SAP Data Insights" Blog (2024).
3. Hong et al. "MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework" ICLR 2024.
4. Wu et al. "AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation" ICLR 2024 workshop.
5. Yang et al. "Collaborative Evolving Strategy for Automatic Data-Centric Development" arXiv preprint arXiv:2407.18690 (2024).
6. Omar et al. "DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines" ICLR 2024.