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Remembering findings helps an AI code reviewer avoid rediscovering the same problems on each pass. It does not tell the reviewer when the job is finished. A reviewer that carries findings forward still needs a bounded review goal, fresh evidence that the goal was checked against the current code, named end states such as complete, blocked, or escalated, and a person who decides which changes to keep. Without a stop condition, a persistent reviewer can keep acting, re-raise findings that no longer apply, or hand work back with no clear result.
Memory and compaction do different jobs
Two mechanisms are easy to confuse. The OpenAI Cookbook example “Building Reliable Agents with Memory and Compaction,” by Wesley Pasfield and Emre Okcular and dated 1 May 2026, separates them. Compaction lets the current run keep going when its context window is finite. Memory lets later runs reuse workflow lessons without replaying the full earlier interaction. Compaction serves one run; memory carries useful knowledge into the next one.
In that example, the investigation memo, reviewed by a human, remains the investigation record. A retained memory is not that record. For a code reviewer, the practical version is to keep retained findings in a store separate from the authoritative review output, and to record where each finding’s evidence came from: which change range, which check, and which run. OpenAI Cookbook, Building Reliable Agents with Memory and Compaction (1 May 2026)
Why a remembered finding is context, not proof
Suppose a reviewer flagged a missing null check in a payment handler two passes ago. If a later commit added the check, a memory that still reads “unresolved” is now wrong. Reporting it again wastes the developer’s time and makes the tool harder to trust. The reverse failure is also possible: a finding marked resolved may have been fixed on one branch and reintroduced later, and the memory will not show it.
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The safer pattern is to treat each remembered item as a hypothesis to recheck against the current source state before it appears in a report. Proof-or-Stop, a preprint by Jek Huang and colleagues (arXiv, 16 July 2026), proposes this for lifecycle transitions. It asks for fresh evidence bound to tracked source state, and it treats states such as “reviewed,” “tested,” or “ready to merge” as claims until that evidence satisfies the relevant gate. Proof-or-Stop preprint (arXiv:2607.14890)
Why memory alone does not stop a loop
Microsoft’s Visual Studio Code documentation describes an agent loop as repeated reasoning, action, and validation. In its example, the agent understands the task, acts on code, validates the result, and then may diagnose and repeat. Microsoft, Understand AI agents The cycle explains how work proceeds. It does not say when work ends. At each pass, a reviewer that remembers prior findings still needs a rule for choosing among three actions: repeat, finish, or hand off.
Two failure patterns follow from that gap. The first is persistence without convergence: the loop keeps acting because nothing defines done. The second is stale repetition: findings from earlier passes are carried forward without being rechecked. A preprint titled “When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents” (arXiv, July 2026) reports that its authors manually confirmed 68 infinite-loop failures across 47 projects, out of 74 potential findings before manual review. Those figures describe that paper’s own analysis. They are not an estimate of how often deployed agents loop. arXiv:2607.01641
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The design below is a proposal assembled from the loop and evidence-gating sources cited in this article. It is not an industry standard, and the sources do not benchmark it against alternatives.
Scope
Define the review goal precisely: the change range, the files or checks in scope, and the rules being applied. “Review the repository” has no natural end. “Check the pull request’s diff against the agreed test suite and the listed security rules” does, because each part can be marked done or not done.
Evidence
Require current output from the agreed checks, tied to the source state they ran against. An agent’s statement that it reviewed or tested something is a claim, not evidence that a gate passed. Proof-or-Stop uses the word “proof” operationally, under a stated trust model in which evidence is mechanically verifiable and bound to tracked source state. That does not guarantee semantic correctness. The authors’ evaluations come from their own implementation and setup, so they should not be read as settled proof that every evidence-gated reviewer is correct. arXiv:2607.14890
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Terminal states
Name every way a run can end. The loop-specification preprint by Sandeco Macedo argues for named terminal states. The labels in the table below are illustrative examples, not a published standard, and the budget row is a design recommendation.
| Terminal state | When to use it | What the reviewer reports |
|---|---|---|
| Complete | The review goal has been checked against fresh evidence at the current source state, and no actionable unresolved findings remain. | The checks that ran, the source state they ran against, and the findings closed in this pass. |
| Blocked | Required evidence cannot be obtained, for example a check cannot run or its output is stale. | What is missing and why. The review is not marked as passed. |
| Escalated | A finding needs human judgment, such as a trade-off between design options or unclear intent. | The finding, its evidence, and the decision that is needed. |
| Budget exhausted | The fixed iteration or time bound is reached before the goal is met. | Reported as blocked or escalated, never as complete, with open items listed. |
Human decision
Make the output inspectable. List each finding with its evidence, which checks passed at which source state, and what remains open. Leave acceptance of changes to the responsible person. Microsoft’s Visual Studio Code documentation states the same principle for agents in that environment: “You remain responsible for directing the task and deciding which changes to keep.” Microsoft, Understand AI agents
Best Value
Comparing review designs on stopping behavior
The sources describe design questions, not a ranked set of products, and they include no head-to-head benchmark of these configurations. The table compares three designs on the axes that matter for stopping.
| Design | Bound | Remembered findings | Terminal states | Auditability and control |
|---|---|---|---|---|
| Memory with an iteration cap only | Fixed iteration or time budget | Reused without a requirement to recheck against current source | Mainly budget exhaustion | Not stated by the sources; depends on the implementation’s logs |
| Evidence gate without memory | Review goal plus evidence gate | Not applicable; each pass checks from scratch | Complete, blocked, or escalated | Check outputs tied to source state |
| Memory, evidence gate, and budget | Goal, gate, and budget together | Rechecked against current source before reporting | Complete, blocked, escalated, or budget exhausted | Findings, evidence, and open items exposed for a human decision |
The second design gives up cross-pass context, so it can repeat work that memory would have saved. The third keeps that context but adds the cost of rechecking it, which is the trade-off the design has to accept.
What the published figures do and do not show
- Sandeco Macedo’s preprint “Stop Hand-Holding Your Coding Agent” (arXiv, 28 June 2026) reports that 70% of sampled loop specifications were verified in the paper’s “autonomous zone,” and that 74% named terminal states. These are the author’s codings of a public corpus of fifty loops, as summarized in the preprint abstract. They describe that corpus, not all agent systems. arXiv:2607.00038
- Aditya Aggarwal and Nahid Farhady Ghalaty (arXiv, 13 July 2026) describe a closed-loop framework that accumulates behavioral rules for coding agents. They report a deployment across 35+ services and 11 recorded working sessions. These figures are self-reported and have not been independently validated. arXiv:2607.13091
- The authors of “When Agents Do Not Stop” report 68 manually confirmed infinite-loop failures across 47 projects, as described above. arXiv:2607.01641
- Anthropic says its autonomy analysis examined “millions of human-agent interactions.” That is the report’s own characterization of its dataset. Consult the report for its scope and methods. Anthropic, Measuring AI agent autonomy in practice
All four items are preprints or vendor reports, and none had independent replication established at the time of writing. Treat the numbers as attributed findings, not as measures of how well any stop condition performs.
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