Scope control
Every assistant task starts with intent, acceptance criteria, risk, and non-goals.
AI-generated code governance
AI-generated code can move quickly, but teams still need ownership, review, testing, security checks, release controls, and durable evidence.
Governance model
Every assistant task starts with intent, acceptance criteria, risk, and non-goals.
Only provide the files, contracts, logs, and data needed for the task.
High-risk actions require explicit human approval before dependencies, migrations, infrastructure, or production steps.
Human reviewers check correctness, maintainability, tests, security, and product fit.
The team records accepted, changed, rejected, tested, reviewed, released, and learned evidence.
Evidence ledger
| Evidence | Why it matters | Owner |
|---|---|---|
| Prompt contract | Shows approved intent, boundaries, and prohibited actions. | Product and engineering |
| Review notes | Shows human judgment, concerns, and final acceptance decision. | Engineering reviewer |
| Test results | Shows what was verified and what was not run. | Engineering or QA |
| Security and dependency review | Shows sensitive risks were checked before release. | Security or reviewer |
| Release and rollback notes | Shows production impact, monitoring, and recovery path. | Operations or release owner |
FAQ
Use workflow routing, prompt contracts, human review, verification, release controls, and evidence ledgers. The assistant proposes; the team decides.
At minimum, keep the prompt contract, review notes, test or check results, acceptance decision, and release or rollback notes for production-impacting changes.
Good governance should reduce rework. The goal is not ceremony; it is to catch scope drift, hidden risk, and unverified code before they become production problems.
Related guides
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