A Safer Agent Review Convention

Use one repo-level convention that tells every coding agent what it may change, which tools it may call, and what evidence must appear in review. This is how shared agent workflows lower code review risk: reviewers see the same checklist, same MCP boundaries, and same handoff whether the author used Claude Code or another agent.

Model Context Protocol is a standard way for coding agents to connect to external tools and data through bounded servers. For ai coding training for teams, the important lesson is not which agent is cleverest; it is whether the team can run the same safe workflow twice.

Paste the convention where agents and reviewers will see it

Start with a short convention in CLAUDE.md, then mirror the review checklist in your pull request template. Claude Code, Anthropic's coding agent, reads repo context; reviewers read PRs. Put the same rule in both places so the agent and the human are not negotiating from different maps.

The trap is making the convention too grand. A five-page policy becomes wallpaper. A small review checklist becomes muscle memory.

# Agent review convention

Lives in: CLAUDE.md and .github/pull_request_template.md
Owner: Engineering Enablement
Applies to: Claude Code, Claude, OpenAI Codex, and any MCP-enabled coding agent used on this repo

Before agent work starts:
- State the task in one sentence.
- Name the files or directories the agent is allowed to edit.
- Name the MCP servers the agent may use.
- Decide whether the agent may create, update, or only suggest changes.

Allowed MCP boundary:
- Read-only: GitHub issues, project docs, design notes, test fixtures.
- Write allowed only through a PR branch.
- Never allowed: production database writes, secret rotation, deployment approval, PR approval, or permission changes.

Required PR evidence:
- The PR says which agent or workflow was used.
- The PR lists tests run, including any skipped tests.
- The PR names any MCP server used for external context.
- The diff avoids unrelated cleanup.
- A human checked security, data access, and public API changes.

Reviewer checklist:
- [ ] Does the change match the original task?
- [ ] Did the agent stay inside the declared file and tool boundary?
- [ ] Are tests meaningful for the risk of this change?
- [ ] Are generated comments, docs, and error messages true?
- [ ] Would this still be safe if repeated by a different agent tomorrow?

This is the smallest useful version of shared agent workflows for code review risk reduction. It gives the agent a lane, gives the reviewer a checklist, and gives the team a way to improve the lane after each miss.

Adopt it through one owner and one review

Treat the convention like production code. One engineering lead proposes it, two frequent reviewers comment on it, and the team lands it in CLAUDE.md, AGENTS.md, or the repo equivalent used by your stack.

Use nested files when the repo has different risk zones. A frontend package can allow screenshot tooling and design MCP context. A payments package should have a narrower boundary and stronger reviewer requirements.

The trap is letting every team invent a private dialect. Cross-tool agentic coding governance works best when Claude Code, Claude, Anysphere's AI code editor, and OpenAI Codex, OpenAI's coding agent, are all asked to follow the same core contract.

For a broader rollout frame, keep this convention next to the related training topic. If you want a companion pattern focused on review lanes, see Shared Workflows for Safer Review.

Test MCP boundaries before reviewers trust them

MCP is powerful because it lets agents fetch real context from GitHub, docs, issue trackers, databases, and internal systems. That also makes it the place where review risk can quietly move outside the diff.

As of June 2026, Ocarina is a useful example from the MCP ecosystem: it automates and tests MCP servers from YAML without an LLM in the loop. The practical takeaway is bigger than that one tool. Teams should test tool boundaries deterministically before asking reviewers to trust an agent's story about what it did.

Use hooks as tripwires, not as decoration. A hook can block a write outside an allowed directory, stop a command that touches secrets, or require tests before a PR-ready slash command finishes.

The trap is treating MCP permission as a chat prompt. Prompts are helpful, but boundaries should also exist in server permissions, repo permissions, CI checks, and review rules.

Train one workflow before adding five agents

Engineering team ai adoption gets easier when the first habit is boring and shared. Pick one workflow: issue to branch, branch to tests, tests to PR, PR to human review. Then teach Claude Code users how to run that path with a slash command, a skill, or a short prompt template.

A simple Claude skill can carry the reusable parts: how to inspect the repo, when to ask before editing, what test evidence to collect, and how to prepare the PR summary. A slash command can make the entry point easy, such as /review-ready, but the standard still lives in the repo so it is visible outside one person's machine.

The trap is confusing tool fluency with team readiness. An engineer who can make an agent produce a diff is not automatically running a safe workflow. The workshop goal is shared judgment, not just faster typing.

Keep the review rule strict and small

The review rule is simple: no agent-assisted PR gets merged unless the reviewer can verify the declared boundary, the test evidence, and the human-owned decision points. If any of those are missing, the reviewer asks for a smaller PR or a clearer handoff.

This protects developer productivity because reviewers stop reverse-engineering the entire agent session. They check the declared contract against the diff.

The limitation is real: a checklist will not catch every logic bug, insecure assumption, or stale requirement. It does reduce avoidable review chaos, especially when multiple coding agents and MCP servers are in play.

Common questions

• Do shared agent workflows for code review risk reduction actually help?

  Yes, shared agent workflows for code review risk reduction help when they make agent behavior reviewable instead of mysterious. The useful artifact is one repo convention plus one PR checklist; the caveat is that it only works if reviewers are allowed to block PRs that skip the declared tool boundary or test evidence.

• Should this live in CLAUDE.md or AGENTS.md?

  Put Claude-specific operating context in CLAUDE.md, and put cross-agent repository rules in AGENTS.md if your toolchain reads it. The important number is one source of truth per scope: root for repo-wide rules, nested files for packages with different architecture, security, or MCP permissions.

• How much MCP access should a coding agent get during review work?

  Give the agent the least MCP access that lets it complete the task with evidence. A safe starting boundary is read-only access to issues, docs, and test fixtures, with writes limited to a PR branch; production writes, secret changes, deployment approval, and PR approval should stay outside the agent lane.

• What should ai coding training for teams cover first?

  Start with the shared workflow, not the fanciest prompt. A practical first session should cover CLAUDE.md, scoped repo rules, MCP boundaries, test evidence, and the review checklist; teams can add skills, hooks, and slash commands after reviewers trust the basic handoff.

• Can we use the same convention across Claude Code, Claude, and Codex?

  Yes, the convention can be cross-tool if it describes outcomes and boundaries instead of product-specific clicks. Keep durable rules in repo files, keep reusable workflow knowledge in skills or commands, and let each product handle its own interface details without changing the review contract.

Further reading

• Model Context Protocol — specification
• Claude Code — getting started
• Claude — Agent
• OpenAI Developers — Codex quickstart
• GitHub — openai/codex
• GitHub — anthropics/skills
• OWASP — Top 10 for Large Language Model Applications
• NIST — AI Risk Management Framework
• Google Search Central — helpful, people-first content
• Google Search Central — generative AI content guidance

Start with one repo

Copy the convention into one active repo, use it for the next five agent-assisted PRs, and revise only the lines reviewers actually used. That is enough signal to turn a policy into a team habit.

One methodology lens

One useful way to read this through our methodology is the Plan step: delegate first-pass decomposition and dependency mapping, review the sequencing and assumptions, and keep ownership of scope and priorities. If that split is still fuzzy, the workflow usually is too.