AI agents: from a goal to a completed task

The action loop

An agent receives a goal, chooses a next step, uses an available tool and examines the result. It may repeat that cycle before returning an answer. A model alone produces output; the surrounding software decides which tools exist, what permissions apply and when execution must stop.

A concrete example

Consider an assistant preparing a weekly project update. It could read approved notes, identify unfinished tasks and create a draft. Each tool result becomes evidence for the next step. If a document is unavailable, the agent should report the gap rather than invent its contents. Sending the update is a separate action with a different consequence.

Design for completion

Define a visible success condition before adding more autonomy: the draft includes every project, dates come from the source and missing information is marked. Add a step limit and a way to resume after an error. Start with a narrow task you can inspect. An agent becomes useful when its actions and final state can be checked, not simply when it produces a convincing account of what it intended to do.

Sources & notes

AI-assisted editorial content checked against the linked sources.

Anthropic: Building effective agents

Sources reviewed for the September 2026 launch edition.