AI Agent

A chatbot answers a question. An agent is given an objective and works toward it: deciding what to do first, calling a tool, reading the result, deciding what to do next, and continuing until the goal is met or it gives up.

The capability that makes this possible is tool use — the model can emit a structured request to run a search, query a database, send an email or execute code, then incorporate the result. Wrap that in a loop and the model can take real action in the world rather than only describing it.

The word is used loosely. Vendors apply "agent" to everything from a genuinely autonomous multi-step system to a chatbot with one API call attached. The useful test is whether the system decides its own sequence of steps, or follows one somebody wrote.

Agents are where AI shifts from advising to doing, which changes the risk profile entirely. A wrong answer wastes your time; a wrong action sends the email, issues the refund, or modifies the record. That is why permissions, spending limits, audit logs and human checkpoints matter far more for agents than for chat, and why reliability over long task horizons is the central open problem.

That "agent" identifies a specific kind of product. The word gets applied to everything from a genuinely autonomous multi-step system to a chatbot with one API call bolted on. There is no industry definition, so the label alone tells you nothing about what you are buying. The question that does: does the system decide its own sequence of steps, or follow one somebody wrote?

That a working demo predicts a working deployment. This is the expensive one, and the arithmetic is unforgiving. An agent that completes each step correctly 95 percent of the time succeeds at a five-step task about 77 percent of the time, and at a twenty-step task about 36 percent. Reliability compounds downward, so per-step accuracy that looks excellent produces a system that fails most long tasks. Demos are short by construction; real work is not.

That more autonomy is the goal. The mature production pattern is not full autonomy — it is an agent that does the work with a person approving the consequential step. That is not a transitional limitation waiting to be engineered away; it is the design that survives that compounding arithmetic, and that keeps a recoverable failure from becoming an irreversible one.

Scope agents to tasks where a mistake is recoverable and visible, and keep a human approving anything irreversible. The mature pattern in production is not full autonomy — it is an agent that does the work and a person who approves the consequential step.