hardCognition Sierra OpenAI

Your agent loops forever or never finishes the task. How do you bound and control agent execution?

An agent that retries the same failed action 40 times is a runaway bill and a stuck user. Termination is something you engineer into the harness, not something the model reliably decides. Here is the control layer.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

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LEARN THE BACKGROUND

These lessons teach the material this question tests, in order and from the beginning.

Agent Engineering·The single-agent loopFree14mStopping is a design decision, not a budgetBudgets stop a runaway agent. They do not tell it when it is finished, and the difference between those two things is where most agent quality is lost. This lesson covers the four ways to define done and what to return when you never got there.Agent Engineering·The single-agent loopFree180mProject: build an agent loop from scratchOpen your editor and build the thing this module described: a bounded loop with three tools, a finish action, and full logging. About three hours, and you will end with an agent you can point at and a trace you can read.

UP NEXT ON YOUR JOURNEY

Next in this trackA tool your agent depends on returns errors or garbage. How do you make the agent robust to tool failures?Next in this trackUsers ask vague, underspecified questions your RAG can't answer well. How do you handle query understanding and clarification?Next in this trackYour retriever misses the relevant document entirely. How do you debug and fix low recall?

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