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If an agent has ever burned through tokens overnight, declared broken code finished, or circled a task it could never complete, you have met one of four predictable failure modes. Each one comes from the design of the loop around the model, not from the model’s raw ability. Loop engineering is the practice of designing that repeated control structure on purpose, so these failures are caught by design rather than discovered on the invoice.
What loop engineering covers
The Loop Engineering project describes the discipline in one sentence in its README: “Prompt engineering shapes a turn. Context engineering shapes what the model sees. Loop engineering shapes the trajectory — the control structure that decides what the model does next, when it stops, and how it recovers.” The project presents this as a methodology rather than a library, and says there is nothing to install. The design questions it raises are therefore the same whatever framework you use.
In practice, every loop needs four decisions made explicitly:
- Observation: how the system measures what the last action actually produced.
- Next action: how it chooses the following step from that observation.
- Stopping: the condition under which the loop ends, succeeds or gives up.
- Recovery: what happens when an attempt fails, including retries and escalation.
Most of the four pitfalls below are a gap in one of these four decisions.
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Pitfall 1: Runaway loops
A loop with no hard stop keeps retrying. Each retry adds model calls, tool calls and tokens, and a loop that is “almost” succeeding can look productive while costing money. The fix is to decide the end of the loop before it starts.
Set a machine-checkable stopping rule before the run
A stopping rule is only useful if software can evaluate it without asking the model. “Stop when the job is done” is not one. “Stop when the test command exits with code 0, or when the attempt limit is reached” is. Write the rule as a check that runs outside the agent’s own reasoning.
Bound attempts, time and spend
The Loop Engineering methodology recommends a global iteration or budget cap for any feedback cycle. In practice, a bounded loop usually sets several limits at once:
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- A wall-clock timeout, so a hung tool call cannot hold the loop open indefinitely.
- A token or cost ceiling, checked after each model call.
- A no-progress rule, such as stopping after two consecutive attempts that produce the same failing output.
When any limit is hit, the loop should exit with a distinct status, such as “budget exhausted”, rather than silently returning its last attempt. A distinct status lets you tell a failed task from a task that was never given enough room.
Pitfall 2: Unverified autonomy
An agent’s statement that its work is complete is not evidence that the work is correct. Models can describe a fix they did not make, or report passing tests they never ran. A loop that trusts this self-report will stop early, and the error will surface later, in someone else’s hands.
Require evidence from an independent or deterministic checker
Good acceptance signals come from outside the model. Examples include test-runner output, a compiler or build result, a schema validator, or a linter run against the changed files. Each of these produces the same answer for the same input, which is what makes it usable as a stop condition.
Confirm the checker can actually fail
A checker that always passes creates false confidence, and this is easy to miss because the loop looks healthy. Before trusting a check, feed it known-bad input and confirm it rejects that input. For a test suite, that means a deliberately broken branch. For a validator, it means a malformed document. If the check accepts the bad input, the loop is not verifying anything.
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An instruction such as “make this better” gives the loop no reliable way to know when it has succeeded, so it either stops arbitrarily or keeps polishing indefinitely. The problem is not that the model lacks effort; it is that the goal has no endpoint.
Turn the goal into criteria a checker can judge
Replace the adjective with measurable conditions defined before the run. For example, “make this better” might become: all tests in the existing suite pass, the linter reports zero errors, and the function handles an empty input without raising an exception. Each criterion should map to a signal the loop can read. Criteria that cannot be measured are wishes, not acceptance conditions.
Add a human escalation point where automatic judgement is impossible
Some goals, such as tone, clarity or design quality, cannot be fully judged by software. For these, the loop should not pretend to finish on its own. Build an explicit escalation step: the loop produces a candidate, packages the evidence, and hands it to a person for approval, with a defined outcome for rejection.
Pitfall 4: Complexity overflow
A single loop can handle a bounded task well and still fail on a large one. As the task grows, the context the model must track grows too, dependencies between sub-steps multiply, and a failure late in the run can force a costly restart. The remedy is to split the work into smaller stages with their own endpoints.
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Decompose into stages or a graph of smaller tasks
Divide the job along dependency lines. Each stage should have one clear input, one measurable output, and one termination condition. Where stages are independent, they can run separately; where they depend on each other, the dependency should be explicit in the structure rather than left to the model’s memory.
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Bound depth and fan-out in recursive designs
When a stage can spawn sub-tasks, the number of levels (depth) and the number of children per level (fan-out) both need hard limits. Without them, a single planning step can multiply into an uncontrolled tree of model calls. Set both limits in the design, and make each sub-task inherit a bounded budget from its parent.
Pass verified, compact results between stages
A handoff should carry the result and the evidence that it passed its check, not the whole transcript of the previous stage. Compact outputs keep downstream context small, and verified outputs stop an early error from propagating silently. If a stage fails its check, the handoff should not happen at all.
Choose between a single loop and a decomposed design
The sources reviewed give design principles, not a numerical threshold for when to split a task. The comparison below uses the axes that matter in practice.
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|---|---|---|
| Task size and dependency structure | Suits a task with one endpoint and few dependencies | Suits large tasks with separable stages and explicit dependencies |
| Measurable endpoint | One checkable end condition for the whole task | A checkable endpoint for every stage |
| Cost and failure impact of retries | A retry repeats the whole task, so failures are costlier per attempt | A retry repeats only the failed stage, but total orchestration overhead is higher |
| Depth and fan-out limits | Not applicable beyond the single loop’s own bounds | Must be set explicitly for recursive or branching designs |
| Verification at handoffs | Not applicable | Each handoff needs its own check before the next stage starts |
| Quantitative benchmark for choosing | Not stated by the Loop Engineering project or the secondary guides reviewed | Not stated by the Loop Engineering project or the secondary guides reviewed |
Matching symptoms to pitfalls
When a loop misbehaves, the symptom usually points to one of the four failure modes:
- Token use climbs while the task stays unfinished: runaway loop (Pitfall 1).
- The agent reports success, but the output fails when someone runs it: unverified autonomy (Pitfall 2).
- The loop stops early or never stops, with no clear reason in the logs: vague goal (Pitfall 3).
- Late-stage failures force restarts, or errors appear far from their cause: complexity overflow (Pitfall 4).
The Loop Engineering README makes the same point in its own terms: the trajectory is the thing to design. Treat each loop as a system with an observation, a decision, a stop and a recovery path, and check each one before you trust it with real work.
Although the methodology is framework-neutral, the same questions apply to any agent you build or run. Ask who or what decides that the work is done, and whether that decision could be wrong.
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