AI Coding Verification

AI-generated code is used in software work, but adoption and review depth vary by team. The task-specific question remains: what did this generated change do, which checks observed it, and what remains uncertain? Unrecorded verification work contributes to what this site calls verification debt.

These survey figures were checked on 17 July 2026 and describe the respondent sample; they do not establish universal adoption, reliability, or control effectiveness. In this article, verification addresses a task-relative question alongside code review: not only “is this code well written?” but “is this the change we asked for, inside the boundaries we set, with a record of the checks and their limits?”

Code review assesses a change through the team’s review method. Verification, as used here, is a task-relative loop around the coding run, with several checks outside the diff:

• Intent match: does the change match the explicit task? This cannot establish every unstated expectation.
• Scope respect: did the run stay inside the files and boundaries it was given, or did it “helpfully” touch things it shouldn’t?
• Validation: did checks run that the generating model did not write itself - tests, linters, type checks, build?
• Evidence: is there a reviewable artifact recording what was checked, the observed results, what was skipped, and what remains uncertain? The artifact is evidence, not proof of correctness.
• Human acceptance: does the named technical decision owner - with that evidence in front of them - accept or reject the change? Business and legal approval remain separate decision rights.

A practical loop that works with any AI coding tool:

1. Before the run - define the task. Write down the goal, the boundaries (which files, which behavior must not change), and the validation plan: how you will know it worked. The appropriate detail depends on task risk and scope.
2. During the run - keep it bounded. Small, scoped runs beat sprawling ones. If the task grows, split it. Larger changes can still be verified, but they increase review and evidence cost and may obscure task boundaries.
3. After the run - check against intent. Diff the result against the written task, not against your memory of it. Off-scope changes are findings, even when they look like improvements.
4. Validate independently. Run the validation plan: existing tests, type checks, lint, build - plus targeted checks chosen for the relevant risks. Different authorship can reduce correlated assumptions but is not sufficient evidence of independence by itself.
5. Attach the evidence. One short record per run: intent, what changed, what was validated, what was not, open questions. This is what the reviewer - and your future self - actually needs.
6. Human gate. A person with the assigned technical decision right accepts or rejects the change with evidence and limitations in view. No auto-commit; this gate is not business, legal, security, or compliance approval.

Several tool categories cover parts of this loop, and most teams will combine them:

• Cloud PR reviewers can comment on a pull-request diff after the code exists. Their coverage, data path and setup depend on the selected product and configuration.
• Static analysis, linters, and SAST apply configured rules and analyses to defined patterns. They can produce evidence about those rules but generally cannot infer an unstated task intent or prove absence of defects.
• Tests and CI verify behavior within their fixtures, assertions, environment and execution. Same-run test authorship may correlate assumptions, but different authorship alone does not establish adequate coverage.
• Verification layers is the editorial term used here for workflows that join declared intent and boundaries, selected validation, evidence, limitations, and a human gate. It is not a claim that one product proves the whole change. Reality Graph is designed to run locally, beside the coding tools you already use.

None of these replaces the others. The mistake to avoid is covering only the post-hoc steps: without an authoritative written task, a later check cannot compare the change with that task as recorded.

Reality Graph is designed to record task boundaries and context, validation results, limitations, and a reviewable evidence report around a human gate. Those artifacts support technical review; they do not prove correctness or security and do not grant business, legal, compliance, or release approval. It sits beside coding tools, tests, analysis, review, and runtime evidence. The six steps that one run goes through, from the written task to the recorded outcome, are documented under how the product works. It is in private beta - early access is open for a small group of teams.