Loop Engineering
AI Engineer path · lesson 86 of 124Engineer the agent control loop for reliability: gather → act → verify with retry-on-failure, bound it with budgets and loop detection, and add production reliability patterns.
Run the loop — watch it verify, fail, and retry.
Key insight
The reliable loop adds a step ReAct skips: verify. Attempt 1’s tests failed, so the loop fed that failure back and retried — and attempt 2 passed. Self-correction comes from checking the work, not from the model getting it right first try.
Keep an agent loop from running away
Sort each control by what it protects.
Tap a piece, then tap where it goes — or drag it. Tap the tray to take one back.
Guarantees termination
Drop items hereLimits the damage
Drop items hereTest your knowledge
What is the most effective way to retry after an agent's output fails verification?
The short version
Loop engineering is the difference between a junior who submits the first draft and a senior who runs the tests, reads the failures, fixes them, and only then calls it done — within a deadline, escalating if truly stuck.
In more depth
Loop Engineering
Where context engineering manages the loop's inputs, loop engineering manages its control flow — making the agent loop reliable. The basic ReAct loop (think → act → observe) becomes production-grade by adding a verify step (tests, rules, or an LLM-judge), retry-with-feedback that appends the specific failure to context, and hard budgets (step/token/time caps + loop detection). A robust loop has four exits — success, budget exhausted, stuck, or escalate — and uses reliability patterns like sub-agents, parallel tool calls, checkpoints/idempotency, and graceful degradation.
Interview Questions
References & Further Reading
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