Platform teams are often asked to make AI coding safe enough for broad use.
That does not mean writing a long policy document and hoping every team follows it. It means turning policy into defaults, gates, evidence, and review paths inside the delivery workflow.
An AI coding agent policy should tell teams what is allowed. A platform implementation should make the allowed path easy to use and the risky path visible.
What the Policy Should Cover
A useful policy answers seven practical questions:
- Which work can agents accept?
- Which repositories can they access?
- What context can they use?
- Which commands can they run?
- What validation must pass before PR/MR handoff?
- Who reviews the result?
- What evidence remains for audit and cost review?
If the policy cannot be mapped to these controls, it will be hard to enforce.
For a broader governance frame, read AI Code Governance Platform.
Start With Approved Work Types
Policy should define which work is eligible for AI coding.
Recommended first categories:
- well-scoped bugs with reproduction steps
- small implementation tickets with acceptance criteria
- tests for existing behaviour
- bounded refactors inside owned services
- documentation and configuration changes with clear owners
Restricted categories:
- authentication, authorization, payments, and encryption changes
- production incident response
- large architecture changes
- work requiring customer data or production secrets
- changes without a named human owner
The goal is not to ban useful work forever. The goal is to make expansion evidence-based.
MergeLoom’s work intake integrations help teams start runs from approved systems rather than ad hoc prompts.
Set Repository Access Rules
Agents should not receive broad access because a developer has broad access.
Policy should define:
- which repositories are approved for agent work
- which teams can request runs
- whether access is read-only, branch-write, or PR/MR handoff only
- whether protected branches are blocked
- how credentials are scoped and rotated
- which repositories require self-hosted execution
For sensitive systems, policy may require workers to run inside the customer environment. MergeLoom’s Self Hosted AI coding infrastructure is built for that pattern.
Define Context Rules
Context is one of the biggest quality and risk levers.
The policy should say:
- which docs, repositories, and rule files are approved context sources
- who maintains repository rules
- how stale docs are handled
- which sources are untrusted task content
- whether external web content is allowed
- how sensitive information is excluded
Do not put all responsibility on the prompt author. Context should be versioned, reusable, and attached to the run record.
MergeLoom’s Context Engine supports this operating model.
Require Validation Gates
Policy should define the minimum checks before review.
Common gates:
- format check
- lint
- typecheck
- unit tests
- build
- custom policy commands
- diff guard for scope and high-risk paths
Not every repository needs the same commands. Platform teams should provide a way to declare repository-specific validation.
MergeLoom’s Quality Agents run validation, bounded repair, review checks, and Diff Guard before PR/MR handoff.
Write Stop Conditions
Many policies only describe allowed behaviour. Platform teams also need stop conditions.
Stop the run when:
- the ticket lacks acceptance criteria
- the repository cannot be identified
- required context is missing
- the agent needs credentials it should not have
- validation cannot run
- repair attempts keep failing
- the diff touches restricted areas
A stopped run with evidence is better than a PR/MR that pushes uncertainty onto reviewers.
Keep Human Review Mandatory
Policy should be explicit: agents do not approve their own work.
The normal code host should remain the approval system. That means branch protection, CODEOWNERS, required checks, review comments, and merge control still apply.
The platform should prepare reviewers with a clear packet: source ticket, summary, validation output, repair history, diff scope, and known gaps.
For a reviewer-focused checklist, see AI generated code review checklist.
Preserve Audit Evidence
Platform policy should define retention and access for AI coding evidence.
Capture:
- requester and source ticket
- repository and branch
- context sources
- commands run
- validation and repair output
- files changed
- PR/MR link
- review and merge outcome
- run cost and accepted outcome status
MergeLoom’s audit trails and attribution are designed around this delivery evidence.
Review the Policy Quarterly
AI coding policy should change as the team learns.
Review:
- which work types are producing accepted PRs/MRs
- which repositories need stronger controls
- which validation failures are common
- which context rules are stale
- which costs are tied to rejected work
- which audit questions are hard to answer
Policy should become more specific over time, not longer by default.
Where MergeLoom Fits
MergeLoom gives platform teams a practical way to apply AI coding policy in the workflow: approved intake, scoped context, validation, repair, review handoff, audit trails, and cost per accepted outcome.
To map policy to controls, start with Ticket-To-Code Automation or book a demo.