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A code diff shows what an AI coding agent changed; it does not prove the change meets the request, avoids regressions, follows team rules, or works in realistic conditions. Evaluate the patch alongside evidence of its outcomes, the agent’s process, and the limits of the checks used.
What a diff can—and cannot—tell you
A diff is a record of textual changes. It helps reviewers inspect implementation, but it cannot establish by itself that the requested behavior now works or that existing behavior remains intact. A small patch can have a broad effect; a large patch can include changes that are irrelevant to the intended result.
That distinction matters especially for agents that act across a repository or interact with tools and environments. A successful-looking sequence of edits or commands is not the same as a verified outcome. For an API or environment task, check the resulting state rather than inferring completion from a trace.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Evaluation frameworks reflect this broader scope. Sourcegraph’s CodeScaleBench distinguishes direct code modification from artifact-based codebase discovery and uses deterministic verifiers for primary scoring. Its design illustrates why source review and outcome checks answer different questions.
What evidence to request with an agent change
- The intended outcome: State what behavior or artifact should exist when the task is complete, along with acceptance criteria and any relevant policy or workflow constraints.
- Verification results: Request relevant tests and deterministic checks where available. Check both the requested behavior and important existing behavior that could regress.
- Process evidence: Review whether the agent used permitted tools and followed required workflow rules. This complements outcome verification; a compliant process can still produce a wrong result.
- Code quality and reliability: Inspect maintainability, edge cases, and unintended behavioral changes. The ACM paper record for ChangeGuard describes execution-based validation for unintended behavior modifications, an example of semantic evidence that can complement a textual diff.
- Context and efficiency, when relevant: If the agent relies on code search or context tools, record whether it found useful files or symbols. Track task reward, retrieval quality, elapsed time, and cost as separate measures.
- Evaluation limits: Record the repository, tasks, harness, provider, verifier, and whether a score came from a deterministic check or a model judge.
Correctness is only one dimension of useful behavior
Passing tests is important, but professional software work also depends on how an agent handles standards, uncertainty, and collaboration. Google Research’s 2026 taxonomy synthesized developer-defined rules and interviews with 15 experienced professional developers into four expectation groups:
- Adherence to standards and processes.
- Code quality and reliability.
- Effective problem solving.
- Collaboration with the developer.
These dimensions help explain why a patch can pass a narrow test and still be a poor contribution—for example, if it violates a required workflow or leaves the developer without enough evidence to review it. They are evaluation criteria, not substitutes for checking the code and its behavior. See the Google Research publication record.
How to compare agent versions or configurations
Give each version the same tasks, acceptance criteria, and comparable information access. Then compare distinct dimensions rather than hiding trade-offs inside one aggregate score.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →| Evaluation dimension | What to compare |
|---|---|
| Outcome quality | Task acceptance, correctness, and regression results. |
| Behavior and policy | Process adherence, tool use, reliability, and collaboration. |
| Coverage | Task types, repository scale, cross-repository context, and edge cases represented. |
| Evidence quality | Deterministic verifiers versus model-judge scores, plus auditability and reproducibility. |
| Efficiency | Cost, elapsed time, and retrieval or tool performance, reported separately from correctness. |
| Generalizability | The tested agent harness, model, tools, benchmark, and verifier limits. |
CodeScaleBench’s report describes 370 software engineering tasks spanning lifecycle work and organizational-scale tasks. In its benchmark setup, Sourcegraph reports a paired reward delta of +0.0349 for MCP versus baseline. For a curated analysis set, it reports retrieval metrics changing from 0.095 to 0.313 Precision@10, 0.120 to 0.272 Recall@10, and 0.091 to 0.240 F1@10. These are vendor-reported findings for that setup, not universal estimates of what code intelligence will do for every agent or repository.
The report’s current results use one MCP provider and one agent harness. That limits what can be inferred across other providers, harnesses, models, or codebases. Keep its reward and retrieval figures in their own categories: better retrieval does not by itself prove a better code change, and a reward score does not explain every behavior a team values. Details appear in Sourcegraph’s CodeScaleBench report.
Proactive agents need a different kind of evaluation
A bounded bug-fix agent can be assessed against an explicit request and acceptance tests. A proactive agent may instead surface a possible issue or opportunity before anyone asks. Its evaluation must consider whether an insight is relevant and supported, whether the timing is appropriate, and whether it should notify, ask a question, draft a change, or remain silent.
Google’s June 2026 Jules article describes a preliminary evaluation using 705 bugs and 1,178 change lists from internal Google codebases. In that evaluation, Hit@5 accuracy rebounded from 33% to 57% when the exploration budget increased from two rounds to three. The authors describe the results as preliminary and say they are expanding coverage to public GitHub data; the figures therefore illustrate an evaluation design, not settled performance across agents or repositories. See “Measuring What Matters with Jules”.
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Keep the limits visible
- A benchmark score describes performance on its task set, harness, provider, and verifier—not every software task.
- A model-judge score is a different kind of evidence from a deterministic verifier; report which produced each result.
- Internal, preliminary results should not be presented as established results for public repositories.
- Tool or policy controls can support safer agent use, but a product announcement does not establish independent comparative performance.
For example, Microsoft’s announcement describes ASSERT and the Agent Control Specification as tools and standards for agent evaluation and control. It supports what Microsoft says they are designed to do, not a claim that they outperform alternatives. See the Microsoft Foundry announcement.
Quick Recap
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