5 techniques for building AI agents that know when to stop
goyalshaliniuk · x · 2026-11-11
A thread on agent reliability engineering arguing that an agent that never stops isn't smart — the goal is making it recognize when it's done. Five techniques: (1) explicit goal completion conditions with measurable criteria; (2) iteration/execution limits — max steps, tool-call caps, timeouts, token/API budgets; (3) detecting repeated actions and stalled progress, e.g. a coding agent rerunning the same failing test; (4) confidence and verification gates — validate outputs, run tests, escalate uncertain results; (5) explicit terminal states: Completed / Failed / Needs approval / Blocked / Budget exceeded. Links out to LangGraph (44.8k stars) and Anthropic's engineering post on designing multi-turn agent evals.
Related event: Engineers Share Techniques to Make AI Agents Stop(2 posts)→
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