{ "version": 1, "type": "ratgeber", "canonicalUrl": "https://tools.utildesk.de/en/ratgeber/multi-model-coding-workflows-codex-gemini-claude-code-review/", "markdownUrl": "https://tools.utildesk.de/en/markdown/ratgeber/multi-model-coding-workflows-codex-gemini-claude-code-review.md", "language": "en", "data": { "slug": "multi-model-coding-workflows-codex-gemini-claude-code-review", "title": "Multi-Model Coding: How Codex, Gemini and Claude Can Review Each Other Well", "date": "2026-07-10T00:00:00.000Z", "category": "Analysis", "eyebrow": "Engineering Workflow", "excerpt": "More coding agents do not create automatic objectivity. They help when planning, implementation, review and accountability stay clearly separated.", "readTime": 11, "coverImage": "/images/ratgeber/multi-model-coding-workflows-codex-gemini-claude-code-review-cover-editorial-v1.webp", "secondaryImage": "/images/ratgeber/multi-model-coding-workflows-codex-gemini-claude-code-review-review-workbench-v1.webp", "tags": [ "AI Coding", "Code Review", "Agents", "Developer Tools" ], "sidebarTitle": "Short take", "sidebarPoints": [ "A second model is a second reader, not a security guarantee.", "Good multi-model workflows split roles, context and approvals, not just prompts.", "Small diffs, tests and a human merge owner still matter more than the model name." ], "relatedTools": [ { "title": "OpenAI Codex", "href": "/en/tools/openai-codex/" }, { "title": "Claude", "href": "/en/tools/claude/" }, { "title": "Gemini", "href": "/en/tools/gemini/" }, { "title": "GitHub Copilot", "href": "/en/tools/github-copilot/" }, { "title": "Cursor", "href": "/en/tools/cursor/" } ], "decisionTools": [ { "title": "OpenAI Codex", "href": "/en/tools/openai-codex/", "note": "a strong fit for terminal-oriented work with clear tests, Git diffs and explicit approvals", "score": "8.7", "kind": "recommend" }, { "title": "Claude", "href": "/en/tools/claude/", "note": "useful as a second reader for specifications, architecture assumptions and explanation-heavy reviews", "score": "8.5", "kind": "recommend" }, { "title": "Gemini", "href": "/en/tools/gemini/", "note": "useful when broad project context, documentation or Google-adjacent work needs a second pass", "score": "8.2", "kind": "recommend" } ], "decisionAvoid": [ "sending three agents at the same vague task and mistaking agreement for quality", "copying an entire repository or production context between providers without filtering it", "allowing an automated agent merge without tests, a code owner and a clear rollback point" ], "decisionNote": "More models only help when each role creates a clear artifact: a specification, a small diff, a test record or a reasoned review.", "wordCount": 1616, "contentMarkdown": "\nA coding agent can move an astonishing amount of work in an hour: read files, write a plan, change code, run tests and prepare a pull request. That is why the tempting idea of chaining three agents together is everywhere: one plans, one implements, one criticizes. It sounds like a small engineering team inside a terminal.\n\nThe useful version of that idea is less theatrical. It does not depend on [OpenAI Codex](/en/tools/openai-codex/), [Claude](/en/tools/claude/) and [Gemini](/en/tools/gemini/) magically understanding one another. It depends on a team forcing different points of view. One agent may propose a change. Another receives only the specification, diff and test output, then tries to disprove it. A human decides whether it belongs on the main branch.\n\nThat does not create an AI jury that votes responsibility away. It creates a repeatable review workflow. This matters most for refactors, migrations, security-sensitive changes and work where a plausible patch is far from a good patch.\n\n## The problem is not one model, but a closed loop\n\nWhen the same agent writes the task framing, plan, implementation and acceptance note, it is easy to create a closed loop. The agent knows its own intention. It can give a convincing explanation of why a patch is correct without proving that the patch meets the actual requirements.\n\nThis is not a criticism of one particular model. Human developers also miss assumptions, edge cases and tests that exercise only the happy path in their own diffs. The difference is speed: an agent can multiply those blind spots across many files quickly.\n\nA second model is therefore not a truth machine. It is a deliberately briefed second reader. Its prompt should not be \"Is this good?\" Better attack questions are concrete:\n\n- Compare this diff with the acceptance criteria. What is missing?\n- Look for side effects in permissions, error handling and data migration.\n- Check whether the tests actually reproduce the described failure.\n- Give three reasons this patch should not be merged.\n\nThis can work with one model in two genuinely separate sessions. Different model families may add another angle, but they do not replace separated context, clear criteria and tests.\n\n## Three roles, four artifacts\n\nA useful multi-model workflow does not merely separate chat windows. It separates accountability. For an ordinary feature, three roles and four visible artifacts are often enough.\n\n| Role | Job | Artifact that remains in the repository |\n| --- | --- | --- |\n| Planner | clarify requirements, risks and test strategy | short specification or issue comment |\n| Implementer | complete one bounded task | small, understandable diff |\n| Second reader | hold requirements, diff and test result against each other | prioritized review and open questions |\n| Human owner | assess risk, product intent and merge readiness | approval or return decision with a reason |\n\nThe roles can be filled by Codex, Claude, Gemini, [GitHub Copilot](/en/tools/github-copilot/) or [Cursor](/en/tools/cursor/). Teams are better off testing them on a real task than choosing a winner from benchmark tables.\n\nFor example, a SaaS product needs an export job that can resume safely. The planner describes state transitions, cancellation paths and the visibility needed when something fails. The implementer gets that specification and a branch of its own. The second reader receives the resulting diff, acceptance criteria and test run. Its job is not to reimplement, but to find missing idempotency, unchecked permissions or silent data loss. Only then does a human look at the pull request.\n\nThat sequence matters far more than which model writes which line.\n\n## Context is working material, not a parcel for everyone\n\nSeveral agents can create a new bottleneck quickly: context gets carried from tool to tool like a heavy suitcase. Pasting entire repositories, logs, credentials or private customer documents into every prompt is expensive and risky. It often makes review worse because the signal is buried in the material.\n\nFor a handoff to a second reader, four things are normally sufficient:\n\n1. one sentence describing the business goal;\n2. accepted non-goals and risks;\n3. the relevant diff and file list;\n4. test, lint and build results.\n\nProject rules help keep that context stable. Claude Code can draw on project rules and memory from `CLAUDE.md`-style files; Gemini CLI uses hierarchical `GEMINI.md` files. For Codex, project-level agent notes, commands and test conventions are equally valuable. The common substance matters most: agents need to know which commands are safe, which files are off limits and how a result is verified.\n\nThese files are not a security boundary. If an agent must not touch production, it still needs restricted permissions, no production tokens and an isolated workspace.\n\n## Worktrees make parallel work reviewable\n\nParallel agents in one working directory are a small disaster with good marketing. One reformats files, another changes the same imports, and a third tests an intermediate state. Soon nobody knows which test result belongs to which diff.\n\nThis is where `git worktree` feels unexpectedly modern. Git can manage several working directories for different branches. An agent works in its own worktree, a reviewer checks a fixed commit or pull request, and the main working tree stays quiet. That is not only tidier; it creates a real rollback point.\n\nIn practice, one ticket can have three places:\n\n- `feature/export-resume`: implementation and tests;\n- `review/export-resume`: diff, specification and second pass only;\n- `main`: the unchanged integration baseline.\n\nThe review agent does not need write access to the feature branch. If it suspects a defect, it describes it, proposes a test or prepares a separate patch. That keeps responsibility for each change clear.\n\n## A simple workflow that works today\n\nThe best starting point is not a fully automated agent orchestra. Pick a ticket that would realistically take a human one or two hours. Then run four rounds.\n\n**1. Write the specification first.** One agent drafts acceptance criteria, non-goals, affected files and test cases. A human removes vague points. Without this step, the later reviewer is only reviewing style.\n\n**2. Implement in an isolated branch.** The implementer may build, but the assignment stays small. It must explain the diff and list the commands it actually ran. On larger work, split the task into two pull requests rather than ten subagents.\n\n**3. Run a cold review.** A different model or fresh session gets no long origin story. It receives the specification, diff and test result. Its job is to break assumptions. Questions about migrations, permissions, error messages, concurrency and unhandled return values are especially productive.\n\n**4. Test and make a human decision.** The merge owner checks open points and automatic gates. An agent may explain a failed test, but cannot argue it away. If the patch has become too large, split it or send it back.\n\nThe result is less heroic than an agent rebuilding an application overnight. It does create artifacts the team can still understand next Monday.\n\n
\n \"Editorial\n
\n\n## What a second model should actually review\n\nThe best review question is rarely \"Can you find bugs?\" It gives the agent too much room for generic remarks. Use a small fixed checklist instead:\n\n- **Contract:** Does the diff satisfy the acceptance criteria and only those criteria?\n- **Boundaries:** Which inputs, permissions, error cases or migration paths are not covered?\n- **Evidence:** Which test proves the most critical claim?\n- **Operations:** What will a human see in logs, monitoring or an error message when it fails?\n- **Rollback:** How can the change be disabled or reverted?\n\nA good review may conclude that a second model was not needed. Normal pull-request review is sufficient for a trivial rename or local UI fix. Multi-model effort pays for itself when the cost of a plausible failure is higher than ten extra minutes of adversarial reading.\n\n## Cost, privacy and the false safety of agreement\n\nSeveral agents cost more than tokens. They distribute context across more providers and create more logs, local checkouts and approval points. Decide in advance which code areas, tickets and test artifacts may go to which tool. Production secrets, customer data and credentials do not belong in a review prompt.\n\nModel agreement is equally dangerous. If two agents receive the same incomplete specification, they can agree very elegantly on the same error. The answer is not a fourth model. It is better input: explicit non-goals, reproducible tests, clear data and permission boundaries, and an owner who may disagree.\n\nWhen teams expand automation, they should do so in stages. Start with read-only review. Then allow a suggested patch in a worktree. Then an automated test run. Direct write access in a critical repository should be the last step, not the first.\n\n## Bottom line: cross-checking is a process, not a model trick\n\nCodex, Claude and Gemini can work very well in one engineering workflow. Their combination becomes valuable only when it makes a clean division of labor visible: someone writes the contract, someone changes little and proves it with tests, someone tries to break the assumptions, and a human owns the merge.\n\nTo start, use one ticket and measure not the number of agents but the quality of artifacts. Was the diff smaller? Were edge cases found earlier? Could someone outside the original session understand the decision? If the answers improve, the workflow is ready for the next step.\n\nContinue with our guides to [coding agents in the developer workflow](/en/ratgeber/coding-agenten-2026-codex-claude-code-und-gemini-cli-im-entwickler-workflow/), [the new bottleneck of AI code](/en/ratgeber/ki-code-ohne-kontrolle-der-neue-engpass-liegt-nicht-im-schreiben-sondern-im-verstehen/), [production-ready agentic developer workflows](/en/ratgeber/wie-agentische-developer-workflows-gerade-produktionsreif-werden-einordnung-prax/) and [agent observability](/en/ratgeber/agent-observability-und-debugging-wie-teams-ki-agenten-nachvollziehbar-machen/).\n\n## Sources and further reading\n\n1. [OpenAI Codex](https://openai.com/codex/)\n2. [OpenAI: Codex CLI Getting Started](https://help.openai.com/en/articles/11096431)\n3. [Claude Code: Subagents](https://code.claude.com/docs/en/sub-agents)\n4. [Claude Code: Hooks](https://code.claude.com/docs/en/hooks)\n5. [Gemini CLI: GEMINI.md context](https://geminicli.com/docs/cli/gemini-md/)\n6. [Git: worktree](https://git-scm.com/docs/git-worktree)\n" } }