Autonomous agents
Definition
Autonomous agents pursue goals over extended horizons with limited human input. They plan, use tools, and adapt when the environment or task changes (e.g. coding agents, research assistants).
They sit at the “high autonomy” end of the agents spectrum: instead of one user turn and one response, they run long loops (plan → act → observe → replan) until the goal is met or a limit is hit. Subagents and reasoning patterns (e.g. ReAct, ToT) are often used inside autonomous agents to structure planning and action.
How it works
The agent starts from a goal (e.g. “implement feature X”). It plans (possibly breaking into steps or sub-tasks), then acts (tool calls, code edits, search). The observe step captures results (tool outputs, errors, state) and feeds back into plan for the next iteration. The loop combines planning, memory (what was tried, what worked), tool use, and often reflection (e.g. self-critique). It runs until a stopping condition: task done, step/budget limit, or human-in-the-loop check. Safety and oversight (e.g. approval gates, rollback) are important when autonomy is high.
Use cases
Autonomous agents are a fit for long-horizon, multi-step work where the system must plan, act, and adapt without step-by-step human input.
- Long-horizon coding agents that plan, edit, and test
- Research assistants that gather sources, summarize, and iterate
- Data pipelines that adapt when inputs or schemas change