Loop Engineering: Designing Agent Loops That Actually Finish

The agent loop is the real unit of engineering in agentic systems. Here is how to design termination, budgets, verification and recovery so loops converge instead of spinning.

Most teams shipping their first AI agent discover the same failure mode: the model is fine, the tools are fine, and yet the system either stops too early with a confident half-answer or spins for twenty steps re-reading the same file. The bug is almost never the prompt. It is the loop.

Loop engineering is the discipline of designing the control flow that wraps a model call: what gets fed in, what counts as progress, when the loop stops, and what happens when a step fails. It is the closest thing agentic systems have to architecture.

The anatomy of an agent loop

Strip away frameworks and every agent loop is the same five stages repeated:

Observe — assemble the state the model needs this iteration (goal, history, tool results, environment facts).
Decide — one model call producing either a tool invocation or a final answer.
Act — execute the tool call in a sandbox with timeouts and permissions.
Evaluate — decide whether the result moved the task forward.
Terminate or continue — check stop conditions and budgets.

Frameworks give you stages one to three for free. Stages four and five are where production quality lives, and they are the ones most implementations skip.

Termination is a design decision, not a default

A loop needs more than one exit. In practice you want four, evaluated in order:

Exit condition	Trigger	What to return
Goal satisfied	Verifier passes (tests green, schema valid, checklist complete)	Final answer plus evidence
Budget exhausted	Step, token, wall-clock or cost ceiling hit	Partial result plus explicit gaps
No progress	N consecutive iterations without state change	Escalation with the stuck diagnosis
Hard failure	Unrecoverable tool error, permission denial, unsafe request	Structured error, never a guess

The critical rule: a model should never be the sole judge of goal satisfaction. Whenever the task has a machine-checkable property — a test suite, a JSON schema, an HTTP 200, a row count — make that the primary exit and let the model's opinion be advisory.

Budgets: three ceilings, not one

Step limits alone are a blunt instrument. Mature loops carry a small budget object and decrement it:

Steps — caps pathological ping-ponging between two tools.
Tokens — caps context growth, which is where cost actually accrues.
Wall clock — the only ceiling that protects a user-facing request or a serverless timeout.

Expose the remaining budget to the model. An agent that knows it has two steps left behaves differently from one that thinks it has infinite runway: it stops exploring and writes the answer. This single change removes a surprising share of timeout failures.

Progress detection beats step counting

The most useful signal in a loop is a cheap fingerprint of state. Hash the tuple of (tool name, normalised arguments, result digest) each iteration. If the same fingerprint appears twice, the agent is looping; if it appears three times, break and escalate.

Complement it with a progress ledger — a short, structured record of what has been established and what remains. Feeding the ledger back instead of the raw transcript both suppresses repetition and keeps the context small.

Verification gates: read-back and the critic

Two patterns carry most of the quality gains:

Read-back verification. After any mutation, re-read the artefact and assert the intended change is present. Never trust a write's own success message.
Bounded critique. One critic pass on the candidate answer, with a fixed rubric and a hard limit of one revision cycle. Unbounded critic loops are the classic way to burn a token budget for a rewording.

Error recovery that escalates instead of repeating

Classify failures before you retry. Transient errors (429, 503, network reset) get exponential backoff with jitter. Deterministic errors (400, validation failure, missing permission) must never be retried identically — change the approach or stop. A useful ladder:

Retry once with corrected arguments.
Switch to an alternative tool or a narrower query.
Reduce scope and return a partial result with the gap named.
Escalate to a human with the exact failing call.

Observability: what to log per iteration

You cannot debug a loop from a final transcript. Emit one structured record per iteration containing the step index, remaining budget, tool name, argument hash, latency, result size, progress fingerprint and the exit condition evaluated. With that you can answer the two questions that matter in an incident review: where did it stop making progress, and why did it stop.

A pragmatic checklist

Every loop has an explicit machine-checkable success condition.
Step, token and wall-clock budgets exist and are visible to the model.
Repeat-state detection breaks the loop before the step ceiling does.
Mutations are verified by read-back.
Retries are classified; deterministic errors change strategy.
Partial results are first-class outputs, not failures.
Per-iteration structured traces are retained.

Where to go next

Loop engineering handles a single agent working a single objective. Once you have several specialists, dependencies and parallel branches, the loop stops being the right abstraction and you need explicit graphs — the subject of our companion piece on graph engineering for AI agents. If your loops are drowning in context instead of steps, start with context engineering.

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