A code diff shows which lines an AI agent changed; it does not establish that the requested behavior works, that existing behavior still works, or that the agent followed the right process. To evaluate an agent change, review the patch alongside evidence of outcomes, regression checks, process adherence, and the limits of the evaluation.
What a diff can—and cannot—tell you
A diff is evidence about a patch: the files and text altered, added, or removed. It is useful for reviewing implementation choices and spotting obvious risks. But it cannot, on its own, prove that the change meets the request or behaves correctly in the surrounding application.
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That distinction matters especially for AI agents. The same visible patch may be correct in one context and wrong in another: the requirement may have been misunderstood, an important edge case may be missing, or an existing workflow may have regressed. Conversely, a change can affect runtime behavior without its implications being obvious from the lines alone.
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CodeScaleBench, a Sourcegraph framework covering 370 software-engineering tasks, makes a related distinction between direct code modification and artifact-based discovery across a codebase. Its design uses deterministic verifiers for primary scoring, reflecting the value of checking outcomes rather than treating source inspection as the whole evaluation. Sourcegraph’s CodeScaleBench report describes its approach.
What to evaluate beyond the patch
1. The intended outcome
State what should be true after the agent finishes: a behavior, a changed artifact, or a resulting system state. Turn that expectation into acceptance criteria that can be checked. If the task includes constraints—such as permitted tools, required review steps, or organizational standards—record those separately from the desired outcome.
2. The result and regressions
Run tests or deterministic verifiers that exercise the requested behavior. Also check important behavior that existed before the change. A test suite can pass while omitting the exact new requirement, so match each acceptance criterion to evidence rather than relying on a green status alone.
For work that changes an API, configuration, or external environment, verify the resulting state directly where possible. A successful-looking command trace is not necessarily proof that the intended state was reached.
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Check whether the agent followed the required workflow, used only permitted tools, and provided enough information to audit its work. Process evidence answers whether the agent acted acceptably; it does not prove that the final result is correct. A compliant trajectory can still end in a faulty change, just as a correct-looking patch can conceal a policy violation.
4. Maintainability and reliability
Review edge cases, clarity, and the risk of unintended behavior changes—not just whether the happy path works. The ChangeGuard paper record describes execution-based validation for unintended behavioral modifications, an example of how semantic evidence can complement textual review. The available paper record supports that high-level description, not a particular performance figure. See the ChangeGuard paper record at ACM.
5. Retrieval and efficiency
When an agent relies on repository search or context tools, ask whether it found the relevant files and symbols. Keep retrieval quality, task reward, elapsed time, and cost as separate measures: they answer different questions, and combining them into a single score can hide trade-offs.
6. Collaboration and judgment
Correctness is one part of useful agent behavior. Google Research’s 2026 taxonomy, synthesized from 91 sets of developer-defined rules and interviews with 15 experienced professional developers, groups expectations into four areas:
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- Code quality and reliability.
- Effective problem solving.
- Collaboration with the developer.
The taxonomy gives teams a vocabulary for evaluating more than whether a patch passes tests. Read the Google Research publication record.
How to compare agent versions fairly
When comparing two agent versions, configurations, or evaluation tools, use the same tasks, comparable repository access, and explicit acceptance criteria. Otherwise, a score difference may reflect a change in the tasks or information available rather than a better agent.
| Evaluation axis | What to examine |
|---|---|
| Outcome quality | Acceptance criteria, 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 | Whether results come from deterministic verifiers or model judges, and whether they are auditable and reproducible. |
| Efficiency | Cost, elapsed time, and retrieval performance, reported separately from correctness. |
| Generalizability | The model, tools, harness, provider, and benchmark limits represented in the evaluation. |
CodeScaleBench illustrates why these distinctions matter. In its 2026 report, Sourcegraph describes 370 tasks across the software-development lifecycle and organizational-scale work. For its benchmark setup, it reports a paired reward delta of +0.0349 (MCP minus baseline). For a curated analysis set, it reports retrieval Precision@10 of 0.095 versus 0.313, Recall@10 of 0.120 versus 0.272, and F1@10 of 0.091 versus 0.240 for baseline and MCP conditions. These are publisher-reported results for that setup, not a general estimate of how much any agent improves with code intelligence. The report’s current results use a single MCP provider and one agent harness; its authors identify broader provider and harness evaluation as future work. Read the full CodeScaleBench report.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Proactive agents need an additional test
A bounded bug-fix agent can be judged against a defined task. A proactive agent also has to decide which potential issue matters and what to do about it. Evaluate whether an insight is relevant, supported by evidence, and well-timed—and whether the appropriate action is to 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. It reports that Hit@5 accuracy rebounded from 33% to 57% when the exploration budget increased from two rounds to three. Those figures describe the preliminary evaluation, not a settled result for proactive agents generally. The article says the work is expanding to public GitHub data. Read Google’s account of the Jules evaluation.
How to report evaluation results without overstating them
For a useful evaluation record, name the tested repository or task set, model and agent configuration, available tools, harness, verifier, and whether any score came from a model judge. Report outcome quality, process behavior, retrieval, and efficiency as separate results. State what the evaluation does not cover, too: an agent that performs well on one task set or harness has not thereby been shown to perform well across codebases, providers, or workflows.
Microsoft’s ASSERT and Agent Control Specification announcement is another example of work aimed at agent evaluation and control. It is a product announcement describing what Microsoft says the tools are designed to do, not independent comparative evidence that one agent is more reliable than another. Read the Microsoft Foundry announcement.
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