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Anatomy of a Good Agent Loop

Five things every well-built agent loop needs — and the specific failure each one prevents. If it cannot tell done from not-done, it is not a loop.

Verdeshell Team · 7 min read · Last reviewed

A loop is not "call the model repeatedly". It is a goal, real tool access, managed context, explicit success logic, and error handling that adapts rather than repeats.

The five ingredients of a well-built agent loopFive requirements feed the loop: a goal with a termination condition, real tool access, context management, explicit success and failure logic, and adaptive error handling. Each row also names the failure that occurs when that ingredient is missing.1Goal + terminationwithout it: loops forever, or stops early2Real tool accesswithout it: nothing to observe3Context managementwithout it: agent loses the thread4Success / failure logicwithout it: cannot tell done from not-done5Adaptive error handlingwithout it: blind retry burns budgetA working loopact · observe · reasonretry until doneremove one → it degradesIf it cannot tell done from not-done, it is a generator being called repeatedly — not a loop.
Remove any one of the five and the loop degrades in a predictable, specific way.

1 · A goal and a termination condition

The agent needs a clear objective and a testable state that ends the work. All tests pass. The build is green. The endpoint returns the expected shape.

Without one, the loop fails in one of two directions: it runs forever, burning tokens on a target it cannot recognise, or it stops far too early on the first output that superficially resembles success.

This is the ingredient teams most often skip, because writing down what "done" means is genuinely hard and postponing it feels cheap.

2 · Real tool access

Run code. Edit files. Run tests. The agent needs to change the actual environment and get real signals back, not produce a description of the change it would make.

The practical test is whether the agent could be wrong in a way the system notices. If every output is prose, nothing can contradict it, and the loop has no corrective force.

3 · Context management

Long loops overflow their working context. Every action and observation adds material, and the useful signal — the goal, the constraints, what has already been ruled out — gets pushed out by transcript.

The fix is active curation: summarise what happened, prune what no longer matters, and carry forward only what the next turn needs. The objective is to keep the agent oriented across many turns rather than to keep a complete record.

This is where long-running agents most often quietly degrade. The loop keeps turning; it just stops making progress, because the agent has lost the thread of what it was doing.

4 · Explicit success and failure logic

Define exactly what counts as done, in a form the system can evaluate. Done and not-done must be distinguishable by the harness, not by a human reading the output afterwards.

This is what actually drives the loop — the check is the thing deciding whether to stop or continue. Everything else is machinery around that decision.

5 · Adaptive error handling

When something fails, the agent should change approach based on what failed. Repeating the same action expecting a different result is not error handling, and it is the single most common way an agent burns a large budget for nothing.

Good handling routes around the failure and keeps progressing: try a different method, narrow the problem, or escalate with a specific question rather than a generic one.

The test

A useful rule of thumb: if the system cannot tell done from not-done, it is not a loop — it is a generator being called repeatedly.

The corollary is where the engineering effort belongs. The loop is the part you build, maintain and improve. The model is the engine you swap when a better one ships.

More in Building with GenAI

Loop Engineering: Design the Loop, Not Every Prompt6 min readGraph Engineering: Coordinating Specialized Agents6 min readLoops vs Graphs: How the Two Patterns Nest5 min read

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