Autonomous agent

An autonomous agent is a model in a loop. It takes a task from a person, plans, calls a tool, reads what came back, and decides what to do next, and it keeps doing that until it judges the job done or hits a stopping condition. The environment, through tool results, is what tells it whether it is making progress.

I reach for it when the path through the task cannot be scripted and the number of steps is genuinely unknown. My digital twin is one. It answers questions about my career from a set of knowledge files loaded whole into its prompt, uses tools to record what it cannot answer, and knows when to stop and hand the conversation to me instead of guessing.

The cost is trust and money. An agent that runs for twenty steps has twenty chances to compound a small mistake, and every step is a model call. It needs guardrails, a maximum number of iterations, and checkpoints where a human can look at what it has done so far before it goes further.

It goes wrong when the tools are badly described or the stopping condition is soft. An agent with a vague tool will call it wrong and then reason its way around the failure instead of reporting it. An agent without a clear definition of done will either quit early or keep going long after the useful work ended.

In a good one I look for tools that are documented as carefully as the prompt, a loop I can watch step by step in the logs, and an explicit hand-off path for when the agent is out of its depth. I would rather an agent say it does not know than produce something plausible I have to check anyway.