Shared Workflows for Safer Review

Use shared agent workflows to make code review safer by making every coding agent follow the same rules, tool boundaries, and evidence checklist before a human approves a PR. The point is not to trust the agent more; it is to make agent work easier to inspect.

A shared agent workflow is a team-owned path for how agents plan, edit, test, use tools, and hand work back to reviewers. In Cursor, Anysphere's AI code editor, that usually means a small set of Cursor rules, an AGENTS.md boundary file, and a review checklist that becomes part of AI coding training for teams.

Put the workflow where the agent can see it

Start with the repo, not a slide deck. Put the durable rules in .cursor/rules/agent-review.mdc and keep local constraints in AGENTS.md near the code they govern.

This matters because agents read files, not vibes. A backend package can say never call payment APIs from tests, while a frontend package can say do not change generated design tokens by hand.

The trap is writing a giant constitution. Long rules get ignored by humans and summarized poorly by agents. Keep the root rule short, then add scoped files where the decision actually happens.

For teams building a broader rollout plan, this fits naturally inside the related training topic: teach the convention once, then let each repo add its own sharp edges.

Draw MCP boundaries before you add tools

MCP is the integration layer that lets coding agents connect to external systems such as GitHub, Slack, docs, databases, design tools, and internal services. Treat each MCP server like an API permission, not like a convenience feature.

A good boundary says what the agent may read, what it may write, and when a human must approve the action. For example, reading GitHub issues may be safe by default, but writing labels, closing tickets, or changing deployment settings should require explicit review.

The trap is giving an agent read/write access and hoping code review catches mistakes later. Reviewers can inspect a diff, but they may not see a Slack message, Jira transition, database mutation, or generated artifact unless the workflow requires the agent to report it.

Test integrations without asking the model to improvise

The useful signal from Ocarina is simple: MCP servers can be exercised from YAML without putting a large language model in the loop. As of June 2026, that is the right instinct for governance work.

Use deterministic tests for the tool boundary, then use the agent for the coding task. For example, a repo can have a YAML test that proves the GitHub MCP server can list issues but cannot merge PRs, and a Cursor review rule that asks the agent to cite that test when it uses the server.

The trap is confusing tool tests with business approval. A passing MCP test says the integration behaves as expected; it does not say the agent should be allowed to perform a risky production action.

Keep one convention across your agents

Many teams use more than one coding agent. Cursor may be the daily IDE workflow, Claude Code, Anthropic's coding agent, may handle deeper terminal tasks, and OpenAI Codex, OpenAI's coding agent, may run in another review or automation path.

The team convention should survive those surfaces. Use the same review checklist, the same MCP boundaries, and the same definition of a risky change, even if each product has its own file format or memory model.

The trap is letting every tool grow its own local folklore. That makes engineering team AI adoption feel fast for two weeks, then expensive when reviewers have to remember which agent follows which rules. For a companion view on team-level rollout, see Govern Coding Agents as a Team.

Paste this review convention

Copy this into .cursor/rules/agent-review.mdc. Then mirror the MCP boundary section into the closest AGENTS.md file for each service or package that has different permissions.

---
description: Agent code review guardrails for risky changes
globs:
  - **/*
alwaysApply: false
---

# Agent review convention

Use this rule when an agent changes production code, build config, data access, auth, payments, deployment, or CI.

Before opening or approving a PR, the agent must leave a review note with:

- [ ] Scope: files changed and user-facing behavior affected.
- [ ] Intent: why this change is needed.
- [ ] Risk: the most likely failure mode.
- [ ] Evidence: tests, type checks, logs, screenshots, or docs reviewed.
- [ ] Rollback: the smallest safe way to undo the change.

MCP boundaries:

- [ ] The agent lists every MCP server it used.
- [ ] The agent separates read actions from write actions.
- [ ] No production write through MCP unless the PR names the approved ticket and human approver.
- [ ] No secrets, tokens, customer data, or private incident details are pasted into prompts or comments.

Human reviewer checks:

- [ ] The PR description includes the agent review note.
- [ ] Risky files have an owner review.
- [ ] Generated code is treated like handwritten code.
- [ ] Tests cover behavior, not only snapshots.
- [ ] Any claim the agent makes is backed by code, tests, logs, or linked docs.

Stop condition:

- [ ] If the agent cannot prove a claim, it must say so and ask for human review.

Adopt it the same way you would adopt a lint rule. One engineer proposes the first version, service owners review the risky-action list, and the team agrees where the rule lives.

The enforcement rule is boring on purpose: no AI-authored PR gets approved unless the checklist is filled in or the reviewer explicitly says why it does not apply. That one habit turns shared agent workflows for code review risk reduction from a slogan into a reviewable practice.

Common questions

• How do shared agent workflows reduce code review risk?

  Shared agent workflows for code review risk reduction work because they make agent output predictable before it reaches a human reviewer. The practical artifact is one checklist: scope, intent, risk, evidence, rollback, MCP use, and owner review for risky files.

• Should MCP tools be available during code review?

  Yes, but only with clear read and write boundaries. A good default is read access for issue context, docs, and code metadata, with human approval for production writes, ticket state changes, deployment actions, or anything that touches secrets or customer data.

• Where should these rules live in Cursor?

  Put repo-wide review behavior in a Cursor .mdc rule and put service-specific constraints in the nearest AGENTS.md. The caveat is scope: a payment service, infrastructure folder, and frontend app usually need different tool boundaries even when they share the same review checklist.

• Is this part of AI coding training for teams?

  Yes. AI coding training for teams should teach the shared workflow, not just prompts or shortcuts. A useful first session is 60 minutes: install the rule, review two real agent PRs, tighten one MCP boundary, and agree on the stop condition.

• What is the biggest limitation of this approach?

  The checklist improves reviewability, but it cannot prove the change is correct by itself. You still need tests, owner review, secure MCP configuration, and engineering judgment; the workflow simply makes missing evidence easier to spot.

Further reading

• Model Context Protocol — specification
• Cursor — Agent
• Claude Code — getting started
• 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
• GitHub — msradam/ocarina

Start with one risky path

Pick one repo, one MCP server, and one class of risky PR. Add the rule, require the checklist for two weeks, then keep the parts reviewers actually used.

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.