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A code review agent should remember only compact, repository-specific knowledge that can be traced to its source and checked against the current code. Start with the repository’s own code, tests, CI configuration, documentation, and agent instructions; add external memory only for useful context those materials do not preserve. Keep retrieval relevant, make memory failures non-fatal, and test the system against the same agent without memory.
Start with the repository, not a memory database
Persistent memory is an additional context source, not a replacement for the files that define how a repository works. Before introducing a store, make sure the agent can inspect the current branch’s code, tests, CI configuration, README, contribution guidance, and applicable agent instructions. GitHub documents several instruction surfaces that may apply to a task, including repository-wide and path-specific Copilot instructions, agent-agnostic AGENTS.md files, and task-specific skills.
This baseline matters because repository artifacts are usually the best evidence of current behavior. A remembered convention can help the agent find the right files or understand why a decision was made, but it should not overrule a test, current implementation, or maintainer instruction.
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Decide what belongs in persistent memory
Keep durable repository knowledge separate from task episodes and reviewer feedback. They have different lifetimes and should not be retrieved or maintained as if they were interchangeable.
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| Memory type | Good candidates | How to use it |
|---|---|---|
| Repository knowledge | Conventions, architectural constraints, or operational facts that are not reliably documented in a single current file. | Retrieve when relevant to analysis; verify against current code and instructions. |
| Task episode | A concise summary of a completed investigation, its outcome, and context that may help with a related future task. | Retrieve only when the new task is meaningfully related; do not treat a one-off outcome as a standing rule. |
| Reviewer feedback | Accepted or otherwise authoritative feedback that can be generalized into a reusable rule. | Extract cautiously, preserve the originating interaction, and distinguish the generalized rule from the original request. |
A request to change one pull request is not automatically a repository-wide policy. In Google’s described workflow, review interactions are analyzed after a pull request is merged, when the interaction is complete and the resulting code can inform which feedback is reusable.
Give every memory a scope, source, and validity check
A stored sentence without provenance is difficult to trust or repair. Use small records that let the agent and maintainers answer: where did this come from, what does it apply to, and is its evidence still current?
| Record field | Purpose |
|---|---|
| Repository identity | Prevents one project’s conventions from leaking into another project’s review. |
| Memory type and scope | Distinguishes a durable rule from a task episode and identifies where the rule applies. |
| Concise claim | States one fact or rule without bundling unrelated observations. |
| Provenance | Points to the originating pull request, discussion, file, or code location so the claim can be inspected. |
| Observed revision or time | Shows when the evidence was recorded and helps assess whether it may have drifted. |
| Validity policy | States when to recheck, review, expire, or remove the record. |
GitHub’s engineering description of its memory work emphasizes this problem: “The core challenge for memory systems isn’t about information retrieval, but ensuring that any stored knowledge remains valid as code evolves across branches and time.” In GitHub’s described approach, memories include code-location citations, which the agent checks against the current branch before using a stored fact. Apply the same principle to a custom system: a citation is evidence to revalidate, not proof that the claim remains true.
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- Load repository context. Identify the repository and branch, read applicable instructions, and inspect the files relevant to the change. Do not begin from a memory result alone.
- Retrieve narrowly scoped records. Search repository knowledge using the task and relevant paths or components. Keep the injected results bounded so unrelated history does not crowd out the code under review.
- Check each candidate finding against current evidence. Before using a remembered rule to raise or suppress a comment, confirm that its source still applies to the branch and that the current code supports the conclusion.
- Optionally filter draft findings. A second targeted retrieval can help check whether a draft comment repeats a resolved issue or conflicts with a known convention. Do not let a memory suppress a finding when current code or tests show a real problem.
- Record outcomes only when useful. Capture a concise episode or candidate learning with provenance; route durable rules through a review or approval policy rather than saving every comment automatically.
The AWS sample architecture separates semantic repository knowledge from episodic task memory and injects bounded results into the agent’s context. Google describes both an initial broad retrieval and a second filtering pass for draft review comments. These are implementation patterns, not requirements for every system; the important properties are relevance, traceability, and verification.
Make memory failures non-fatal
A memory service should improve a review, not decide whether a review can happen. If loading fails, times out, or returns nothing, continue with repository context and make the limitation visible to the reviewer. If a write fails after the review, do not turn that into a false claim that a lesson was saved.
The AWS sample describes fail-open reads and writes, retries, and cold-start behavior. A practical policy is to bound retries and latency, log memory availability separately from review outcomes, and never block the code review solely because memory is unavailable. On a cold start, the agent can still inspect the repository artifacts that are present.
Protect memory from poisoning and drift
Persistent memory can preserve a bad assumption as readily as a useful convention. The AWS sample identifies memory poisoning, malicious pull-request comments, hallucination crystallization, error feedback loops, and stale context as risks; GitHub also describes changing code, abandoned branches, and conflicting observations as challenges.
- Treat issue text, pull-request comments, and tool outputs as untrusted data, not instructions to the agent.
- Namespace records by repository and retain provenance so maintainers can inspect how a claim entered memory.
- Gate writes: distinguish accepted or authoritative feedback from a suggestion, rejected finding, or one-off request.
- Re-ground claims in current code and instructions before they affect a review.
- Provide maintainers with ways to inspect, correct, expire, and delete records.
- When observations conflict, surface the disagreement or prefer current repository evidence rather than silently merging incompatible rules.
These controls reduce risk; they do not guarantee that a memory system cannot be manipulated or become stale.
Best Value
Evaluate memory against a no-memory baseline
Run the same review tasks with memory enabled and disabled, holding the rest of the setup as constant as practical. Measure both gains and regressions rather than treating more comments or higher acceptance alone as proof of better reviews.
- Reviewer-rated usefulness of comments.
- Repeated rejected findings and duplicate comments.
- Whether accepted, applicable rules are used correctly.
- Missed issues and false positives.
- Stale-memory incidents and conflicts with current code.
- Review latency and operating cost.
GitHub reported a 7% increase in pull-request merge rates for its Copilot coding agent in 2026: 90% with memories versus 83% without. It also reported a 2% increase in positive feedback on code-review comments: 77% with memories versus 75% without. GitHub reported p-values below 0.00001 for both increases. These are results reported by GitHub for its own systems, not independent results or a forecast of the effect a custom agent will achieve.
Decide whether to build or adopt
Compare options using the controls and operating requirements that matter to your repository, not memory retrieval alone.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors| Decision area | Questions to resolve |
|---|---|
| Ownership and scope | Who owns the memories? Are they scoped to a repository, user, or organization, and who can administer them? |
| Evidence and freshness | Can each fact cite its source, and does the system verify that source against current code? |
| Integration | Does it connect to the code host and relevant issue trackers, documentation, service catalogs, or incident systems? |
| Policy control | Can you customize extraction, retrieval, approvals, and review behavior? |
| Data handling | Where is memory stored, who can view it, and how can it be corrected or deleted? |
| Operations | What happens on an outage, and can you observe quality, latency, and cost? |
GitHub documents Copilot code review’s support for relevant MCP servers and agent skills, while Google describes a managed GitHub review workflow that derives rules from pull-request feedback. Product capabilities, rollout conditions, and availability can change, so check current vendor documentation before relying on a particular integration or feature.
Keep human review in the loop
Memory can make an agent more consistent without making it authoritative. GitHub’s code-review guidance says: “Copilot is not guaranteed to spot all problems or issues in a pull request. Sometimes it will make mistakes. Always validate Copilot’s feedback carefully. Supplement it with a human review.” Apply that standard to a custom review agent too: people should validate consequential findings and retain responsibility for the final review.
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