Autonomous Agent: Definition and Examples

An autonomous agent (agent autonome) refers to an AI program that goes beyond simple text generation: it can plan a sequence of actions, execute them, observe the results, and then adjust its strategy accordingly. Unlike a classic chatbot that responds to a single query, the autonomous agent pursues a goal over multiple steps, using external tools (web browsing, code execution, API calls) to accomplish complex tasks.

The functioning of an autonomous agent relies on a perception-decision-action loop. It receives a mission, breaks it down into subtasks, chooses the appropriate tools for each step, executes actions, and then evaluates whether the goal is achieved or if it needs to modify its approach. This ability for self-correction and iterative reasoning is what fundamentally distinguishes an agent from a simple language model.

In prompt engineering, autonomous agents represent a major evolution. Instead of writing a single prompt and hoping for a perfect response, one defines a high-level objective and lets the agent orchestrate the necessary steps itself. This implies new skills: knowing how to formulate clear objectives, define relevant constraints, and choose the right tools to make available to the agent.

Frameworks like LangChain, AutoGPT, CrewAI, or the Claude Agent SDK allow building autonomous agents. These systems are increasingly used in enterprises to automate complex workflows: research documentation, data analysis, report generation, or AI-assisted software development.