Remembering a finding does not tell a code reviewer when its work is finished. A persistent reviewer can carry forward what it already flagged, but memory gives it no basis for deciding that the review is complete. That decision needs a separate rule with four parts: a bounded review goal, current verification evidence, explicit terminal states, and a human who decides which changes to keep. The rule described below is a design recommendation synthesized from the sources cited here. It is not an adopted industry standard.
Memory and compaction do different jobs
OpenAI’s cookbook example on building reliable agents separates two mechanisms that are easy to confuse. 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. The example is from the OpenAI Cookbook, published 1 May 2026 by Wesley Pasfield and Emre Okcular: Building Reliable Agents with Memory and Compaction.
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In that example, the generated memo is the investigation record, and a human reviews it before it counts as the source of truth. Applied to code review, this points to a practical separation. Keep retained findings in a store apart from the authoritative review artifact, and record where each finding came from: the file, the commit, and the check that produced it. A later pass can then tell an observation it remembers apart from one it has verified against the current code.
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Two ways a persistent reviewer goes wrong
It repeats findings that no longer hold
If a reviewer treats a remembered finding as settled, it may report a problem in code that has since changed, or keep reporting a problem that was already fixed. Memory supplies context for the next pass. It does not supply proof that the finding is still true. The fix is to recheck each retained finding against the current source state before it re-enters the report.
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It keeps acting without a reason to stop
A persistent reviewer can also run indefinitely. Each pass may generate new suggestions, new file reads, or new handoffs, and nothing in the memory itself says that the goal has been met. A stop rule has to be defined separately from the memory mechanism.
Why an agent loop needs its own completion rule
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 the code, validates the result, and then may diagnose the outcome and repeat. The documentation describes that cycle; it does not specify when the cycle should end. A code reviewer that remembers prior findings therefore still needs a rule for three decisions: repeat, finish, or hand off. Source: Understand AI agents, Visual Studio Code documentation.
Sandeco Macedo’s 2026 arXiv preprint, “Stop Hand-Holding Your Coding Agent,” argues for specifying loops explicitly, including named terminal states, rather than relying on step-by-step prompting. The preprint is available on arXiv.
A four-part stop condition
A workable policy for a code reviewer can be written in four parts. Each part answers a question the memory store cannot answer on its own.
1. Scope
State the change or review goal, and list which files and which checks are in scope. A goal such as “review the changes in this pull request against the agreed test suite and the security checklist” is bounded. A goal such as “keep improving the code” is not. A bound can be expressed as a set of scoped checks, an iteration count, a time limit, or some combination of these.
2. Evidence
Completion requires current results from the agreed checks. An agent’s statement that it reviewed or tested something is not the same as a passing gate. The Proof-or-Stop preprint by Jek Huang and colleagues proposes that lifecycle transitions such as “reviewed,” “tested,” or “ready to merge” be treated as claims unless fresh, mechanically verifiable evidence is bound to the tracked source state. Its use of “proof” is operational, under a stated trust model, and does not guarantee that the code is semantically correct. See Proof-or-Stop on arXiv.
3. Terminal states
Name the outcomes in advance, and do not let the loop end without one of them. The table below uses labels that serve as editorial examples. Macedo’s preprint argues for named terminal states in general; the specific labels are a choice for the implementer.
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The reviewer should make its output inspectable and leave the final decision on code changes with the responsible person. The reviewer proposes; the human accepts or rejects. This is the point at which a reviewer’s report becomes a decision record instead of an automated opinion.
Terminal states
| State | Use it when | What the reviewer reports |
|---|---|---|
| Complete | The bounded goal has been checked against fresh evidence from the agreed checks, and no actionable unresolved findings remain. | The scope, each check run with its source state, and how each finding was closed. |
| Blocked | Required evidence cannot be obtained, for example because a check cannot run or the source state cannot be read. Reaching the iteration or time bound without complete evidence also belongs here. | Which check could not run, why, and what is needed to unblock it. The reviewer does not claim completion. |
| Escalated | A finding requires human judgment, such as a design trade-off, unclear intent, or disagreement between checks. | The finding, the evidence behind it, and the specific decision a person needs to make. |
The distinction between blocked and escalated matters. A blocked loop is waiting for something the system can obtain. An escalated loop is waiting for a person.
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One pass, step by step
- Load the retained findings together with their recorded provenance: file, commit, and check.
- Compare each finding’s recorded source state with the current source state.
- Re-run the agreed checks for any file that has changed. Do not carry a changed finding forward as verified.
- Record new findings, and classify each one as actionable or as needing human judgment.
- Evaluate the result against the terminal-state table. If the loop is not complete, blocked, or escalated, it has not finished, and the next pass starts from step 1.
The loop still has to be bounded at step 5. Without the iteration or time limit described above, a pass that never reaches a terminal state will run indefinitely.
Comparing review designs
The sources do not include a head-to-head benchmark of reviewer designs. The table below turns the four comparison axes into design questions. It is not a ranking of tools.
| Design | Bound type | Freshness of remembered findings | Terminal states | Auditability and control |
|---|---|---|---|---|
| Fixed budget only | Iteration or time limit | Not addressed by the bound. Remembered findings can be reused without a source-state check. | Budget exhaustion must be defined separately from completion. | Depends on what each pass logs. |
| Evidence gate only | No inherent limit on passes | Evidence must be bound to the current source state. | Gate pass or fail. The behavior when evidence is unavailable must be defined. | High when evidence is recorded alongside each claim. |
| Bound plus evidence gate, with named terminal states | Scope, iteration or time limit, and checks | Each retained finding is rechecked against the current source state. | Complete, blocked, and escalated, as in the table above. | The reviewer exposes evidence, and a human decides which changes to keep. |
What the reported figures show and do not show
Several recent papers and one vendor report include figures relevant to loop behavior. They are useful as indications of what the authors measured. They should not be read as prevalence across all deployed agents. None of these figures has been independently replicated in the sources reviewed for this article.
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| Figure | Source | Scope and qualification |
|---|---|---|
| 70% of sampled loop specifications verified in the paper’s “autonomous zone” | Sandeco Macedo, 2026, arXiv:2607.00038 | The author’s coding of a public corpus of fifty loops, as summarized in the preprint abstract. It describes that corpus, not all agent systems. |
| 74% of loop specifications named terminal states | Sandeco Macedo, 2026, arXiv:2607.00038 | Same corpus and coding as above, as summarized in the preprint abstract. |
| 68 manually confirmed infinite-loop failures across 47 projects, from 74 potential findings | “When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents,” July 2026, arXiv:2607.01641 | Results of the authors’ manual review. The 74 figure is the count before manual review. The paper does not measure prevalence across all deployed agents. |
| 35+ services in a reported microservices platform, and 11 recorded working sessions | Aditya Aggarwal and Nahid Farhady Ghalaty, 13 July 2026, arXiv:2607.13091 | The authors’ reported deployment and sessions. The results are not independently validated. |
| “Millions of human-agent interactions” examined in an autonomy analysis | Anthropic, “Measuring AI agent autonomy in practice,” report | Anthropic’s characterization of its dataset. Consult the report for its scope and methods before citing the figure. |
Keep the human on the decision
Microsoft’s documentation states the principle directly: “You remain responsible for directing the task and deciding which changes to keep.” That sentence is official documentation language, not a quotation from a named individual.
In practice, a reviewer meets this principle when its output shows a person what they need to decide:
- Each finding, with the file and line it refers to.
- The evidence behind each finding, including the check that produced it and the source state it was run against.
- Which findings are verified, which are carried over from memory and not yet rechecked, and which are open.
- The terminal state of the loop, and, where the state is blocked or escalated, the specific action needed.
- Which proposed changes have been made and which are waiting for acceptance.
A reviewer that keeps its memory, checks it against current code, and stops at a named state gives the person in the loop something concrete to accept or reject. Memory alone does not do that.
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