LLM-powered autonomous agents are evolving beyond single agents and static multi-agent systems with the emergence of frameworks like Autogen and CrewAI. This technique allows developers to break down a complex activity into several smaller tasks performed by agents where each agent is assigned a specific role. Developers can use preconfigured tools for performing the task, and the agents converse among themselves and orchestrate the flow. The technique is still in its early stages of development. In our experiments, our teams have encountered issues like agents going into continuous loops and uncontrolled behavior. Libraries like LangGraph offer greater control of agent interactions with the ability to define the flow as a graph. If you use this technique, we suggest implementing fail-safe mechanisms, including timeouts and human oversight.
LLM-powered autonomous agents are evolving beyond single agents and static multi-agent systems with the emergence of frameworks like Autogen and CrewAI. These frameworks allow users to define agents with specific roles, assign tasks and enable agents to collaborate on completing those tasks through delegation or conversation. Similar to single-agent systems that emerged earlier, such as AutoGPT, individual agents can break down tasks, utilize preconfigured tools and request human input. Although still in the early stages of development, this area is developing rapidly and holds exciting potential for exploration.
As development of large language models continues, interest in building autonomous AI agents is strong. AutoGPT, GPT-Engineer and BabyAGI are all examples of LLM-powered autonomous agents that drive an underlying LLM to understand the goal they have been given and to work toward it. The agent remembers how far it has progressed, uses the LLM in order to reason about what to do next, takes actions and understands when the goal has been met. This is often known as chain-of-thought reasoning — and it can actually work. One of our teams implemented a client service chatbot as an autonomous agent. If the bot cannot achieve the customer's goal, it recognizes its own limitation and redirects the customer to a human instead. This approach is definitely early in its development cycle: autonomous agents often suffer from a high failure rate and incur costly AI service fees, and at least one AI startup has pivoted away from an agent-based approach.
