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The Exit Condition Pattern: How to Stop Your AI Agent at the Right Moment

Most AI agent problems are not about what the agent does. They are about when it stops. I run five AI agents at Ask Patrick — an orchestrator, a growth agent, an ops agent, a support agent, and a research agent. After several hundred h…

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Most AI agent problems are not about what the agent does. They are about when it stops.



I run five AI agents at Ask Patrick — an orchestrator, a growth agent, an ops agent, a support agent, and a research agent. After several hundred hours of runtime, the most common failure mode is: an agent that does not know it is done.






What Happens Without an Exit Condition



An agent without a clear exit condition will:




  • Keep refining work that was already good enough

  • Ask clarifying questions that delay completion

  • Generate unsolicited "bonus" output

  • Loop until hitting a token limit or context window



None of these look like failure. They look like diligence. But they cost money, waste time, and make the agent unpredictable.






The Pattern: Write Done Before You Write the Task



Before writing a task description for any agent, write this sentence first:




"This task is complete when ___."




Fill that blank with something observable and binary — something that does not require the agent to judge whether it is good enough.



Bad exit conditions (require judgment):




  • "When the email sounds professional"

  • "When the analysis is thorough"

  • "When the response is helpful"



Good exit conditions (observable):




  • "When a draft email is saved to /drafts/email-2026-03-08.md"

  • "When the analysis includes exactly three recommendations with supporting data"

  • "When the user has received a reply containing a resolution or escalation path"






Why This Works



Agents do not get tired. They do not have an intuition that says "this feels done." They have a context window that fills up and a pattern-matching system trying to satisfy the prompt.



If your prompt is open-ended, the agent will keep satisfying it — indefinitely, or until something external stops it.



An explicit exit condition gives the agent a terminal state. Instead of asking "is this good enough?", it asks "have I reached the exit condition?" That is a question it can actually answer.






The 30-Second Audit



Look at the last three tasks you gave an agent. For each one, ask: "Would the agent have known, on its own, when to stop?"



If the answer is no — you do not have an exit condition problem yet. You have the beginning of a reliability problem.



Fix it before you scale.






Ask Patrick publishes daily patterns for AI agent operators. The Library has 27 battle-tested configs, updated nightly. askpatrick.co

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