A Claude agent loop repeatedly takes an action, reads the result, and decides what to do next. Self-correction becomes useful when that result contains evidence about the task: a failed assertion, a screenshot with a clipped label, or a missing citation. Memory preserves the lessons that remain useful after the immediate conversation ends.
This guide focuses on the workflow rather than promising a particular model's availability or a universal improvement factor. A stronger model can still spend an afternoon polishing the wrong behavior when its success criteria are ambiguous. Start with a small task whose result another person can inspect, then build the loop around that result.
TL;DR: What does a reliable loop need?
| Question | Practical answer |
|---|---|
| What starts the loop? | An observable goal with a bounded scope. |
| What makes it self-correcting? | Feedback from a check that can fail independently of the author. |
| What should memory contain? | Verified decisions, their evidence, and the conditions under which they apply. |
| When should it stop? | Success, an explicit budget boundary, or a blocker that requires a different decision. |
| Do you always need several agents? | No. Add an independent reviewer when its evidence justifies the coordination cost. |
How do you turn a vague request into a checkable goal?
Consider a request to improve a signup page. The agent could rewrite headings, change spacing, or add an animation. None of those actions proves that signup works. A better goal describes the user journey: a visitor enters a valid address, receives a clear response, and can recover from a rejected submission without losing their input.
Write the acceptance criteria before asking for implementation. Specify which route is in scope, which existing behavior must remain, and which checks the agent can run. A screenshot checks layout; a browser interaction checks the form; a build checks compilation. These checks answer different questions. Passing one does not authorize a claim about all three.
Keep the first objective narrow enough to finish without redesigning the whole application. Ask the agent to inspect the page and identify its current behavior, implement the smallest suitable correction, run the relevant checks, and report any remaining limitation. This creates a concrete feedback path without requiring a complicated orchestration framework.
A useful starting prompt is:
Improve the signup form on /newsletter.
Preserve the existing email submission contract.
Success means: visible field labels, keyboard submission,
clear invalid-address feedback, and retained input on errors.
Inspect the current implementation before editing.
After each change, run the relevant check and show its result.
Stop if the API contract is unclear or a check needs credentials.
The prompt intentionally distinguishes decisions the agent can make from questions it cannot answer through local inspection. That boundary helps prevent an apparently productive loop from filling missing information with guesses.
What evidence should a verifier receive?
Anthropic's evaluator-optimizer pattern describes a generator and an evaluator exchanging work and feedback. For a coding task, adapt that pattern by giving the evaluator the actual change, the acceptance criteria, and the check results. Do not send only the implementation agent's explanation of why the change is correct.
The reviewer should be able to identify a specific defect. “This could be better” gives the author no reliable next action. “The error message is only visible through a hover tooltip, so keyboard users cannot discover it” points to behavior that can be reproduced and corrected. Ask for the location, observed behavior, and expected behavior together.
Independence improves perspective, but it does not guarantee correctness. Two agents can share the same mistaken assumption. Where possible, anchor the evaluation in executable assertions, source documents, or direct interaction. Let an agent interpret that evidence rather than treating its confidence as evidence by itself.
For content work, provide the source alongside the draft. A verifier can then distinguish a supported claim from an inference and find citations that do not actually establish the attached statement. Word count alone cannot catch that mistake; an article can be long, polished, and still misrepresent its sources.
How should the loop react to a failure?
Record the failing check before changing anything. Capture the command, the relevant error, and the state of the artifact being tested. Otherwise the next attempt may fix a symptom without establishing whether the original problem disappeared. A short failure record is also easier for a reviewer to assess than a long narrative about the investigation.
Then form one hypothesis. If the mobile layout clips a button, inspect the containing element and its width rules before changing several unrelated styles. If an extraction task misses a field, inspect the source document and schema before raising the retry limit. Each additional uncontrolled change makes the result harder to explain.
Run the smallest check that addresses the hypothesis, followed by any broader check needed for the changed surface. A passing focused assertion can show that an error was corrected, while a page walkthrough can reveal that the fix made the adjacent text unreadable. The loop should preserve both results rather than collapsing them into a single “all good.”
Avoid retries that merely restate the same request. If the same dependency failure occurs again, identify what new information the next attempt would add. A missing account permission will not be solved by more reasoning. Record the dependency and continue only with work that does not require it.
What belongs in agent memory?
Anthropic's context engineering guidance discusses compaction and structured notes for longer tasks. An effective project note preserves information that would be expensive or dangerous to rediscover: the active objective, a relevant interface contract, a verified failure mechanism, or an unresolved decision.
Treat memory as a maintained record with scope. “Use the app-local validator and pass paths relative to the content directory” is useful when it names the application and the validator. “Always run this command” is fragile because a future project may have a different layout. Include the condition that makes the advice applicable.
Separate observations from hypotheses. A log showing that a request timed out is an observation. A claim that the timeout was caused by a provider limit is a hypothesis until another source establishes it. If memory stores both in the same confident sentence, the next session may repeat an unverified explanation as established fact.
A practical note includes the date, the artifact inspected, the conclusion, and a pointer to the supporting evidence. It should also say what remains open. You do not need to copy the entire terminal history: preserve the few details that allow another person or agent to reconstruct the decision.
Do not store credentials or private customer content merely because it appeared during a task. Prefer a reference to the authorized location and a description of the relevant contract. Memory should make future work easier without becoming an uncontrolled archive of everything the agent encountered.
How can you tell whether a loop is improving?
Use a small set of representative tasks and keep their acceptance criteria fixed. Include an easy case, a realistic case, and a case designed to expose a known weakness. For a signup flow, that might mean a valid address, an invalid address, and a server error. This gives the loop a reason to exercise recovery rather than optimizing only the happy path.
Track the result of each attempt along with elapsed time and resource use when those measurements are available. A lower failure rate is valuable, but it must be compared with the cost of obtaining it. If a reviewer doubles the runtime and catches no additional defects on your tasks, simplify the design before adding more reviewers.
Check for false passes. A test that asserts the mocked function returns the value you assigned to it cannot establish that the real integration works. Likewise, an agent that rewrites its own rubric after every failure can make progress appear better by moving the target. Keep the grading contract separate from the artifact under evaluation.
A useful experiment changes one part of the workflow at a time: the prompt, tool description, retrieval policy, or verifier. Keep the other parts stable long enough to observe what changed. These are local results for your task set; do not turn them into a universal ranking of models or agent architectures.
How do you hand a long task to the next session?
At a checkpoint, write a short continuation note that identifies the current artifact, completed checks, and remaining work. Include the exact failing condition if a check is still red. State whether changes have been committed or deployed, because those are separate states from local editing.
The next session should confirm cheap facts that may have drifted. Re-read the file before editing it, inspect the current working tree, and verify that a remembered route or command still exists. This is especially important when people or other agents share the workspace. Memory guides the inspection; the live state governs the change.
A good handoff ends with the next concrete action and the evidence it should produce. “Run the mobile form check after correcting the error label” is actionable. “Continue improving the app” discards the structure that made the loop useful in the first place.
Related reading
- Loop engineering with coding agents
- Understanding agent stop reasons
- Writing reliable MCP tool descriptions
- Claude Code in CI/CD
- How to catch model hallucinations
Workflow guidance reviewed October 3, 2026. Model availability and product-specific commands should be checked against the documentation for your account and installed version.