Building a Multi-Agent Coding Pipeline: Claude Code, Cursor, and Beyond

A multi-agent coding pipeline splits software work across specialized AI agents (planning, implementation, review, testing) that pass structured artifacts to each other instead of relying on one model doing everything in a single chat. The practical version today combines Claude Code or Cursor's agent mode for implementation with a separate reviewer agent and a deterministic test gate, orchestrated by scripts, not vibes.

What problem is a multi-agent pipeline actually solving?

Single-agent workflows fail in predictable ways once a codebase gets past toy size. Context windows fill with irrelevant files, the model loses track of earlier decisions, and it starts contradicting itself across a long session. A multi-agent setup addresses this by giving each agent a narrow job and a fresh, curated context rather than one giant accumulating conversation.

The other real problem is verification. A single agent that writes code and then "checks its own work" in the same context tends to rubber-stamp itself. Anthropic's own documentation on Claude Code notes that separating the actor from the reviewer, even when both are the same underlying model, produces materially different critique behavior because the reviewer isn't anchored to its own prior reasoning. I've seen this firsthand: a Claude Code session that writes a database migration and then reviews it in a fresh call will catch missing rollback logic that it never mentioned while writing the migration in the first place.

A concrete pipeline architecture that works

The pipeline I run on production repos has four stages:

• Planner agent (Claude Opus or GPT-5 class model): takes a ticket, reads relevant files via a scoped search, and produces a structured implementation plan as JSON or Markdown with explicit file-level changes.
• Implementer agent (Claude Code or Cursor agent mode): executes the plan file-by-file, running local builds after each change.
• Reviewer agent (a fresh context, ideally a different model): diffs against the plan, checks for scope creep, and flags anything not covered by tests.
• Gate script (deterministic, not an LLM): runs lint, type checks, and the test suite, and only then allows a PR to open.

A minimal version of the handoff contract between planner and implementer looks like this:

1{
2 "ticket": "AUTH-482",
3 "files": [
4 {"path": "src/auth/session.ts", "change": "add refresh token rotation"},
5 {"path": "src/auth/session.test.ts", "change": "add rotation expiry tests"}
6 ],
7 "constraints": ["no new npm dependencies", "must not break existing session cookie format"],
8 "done_when": "all tests pass and rotation is logged via existing logger"
9}

That constraints field matters more than it looks. Without it, implementer agents routinely "improve" adjacent code, adding a dependency or refactoring a file nobody asked about. Claude Code respects scoping instructions reasonably well when they're explicit in the prompt or in a CLAUDE.md file at the repo root, as described in Anthropic's Claude Code documentation. Cursor's equivalent is .cursorrules or the newer project rules format, covered in Cursor's docs. Both are necessary but not sufficient. I still see scope creep roughly one time in five even with a well-written rules file, so the reviewer stage isn't optional.

Where Claude Code fits versus Cursor

Claude Code is a terminal-native agent that operates on the filesystem directly, runs shell commands, and can chain multi-step tool calls without a human clicking "accept" on every diff if you run it in an auto-approve mode. That makes it a good implementer stage: you can script it, pipe a plan into it, and let it run unattended against a sandboxed branch.

Cursor is an IDE. Its agent mode is excellent for the human-in-the-loop parts of the pipeline: reviewing diffs inline, jumping to definitions while reading a proposed change, and doing quick one-off edits where spinning up a whole pipeline is overkill. Cursor's composer and agent features are documented at cursor.com, and the tab-completion and inline chat remain the fastest way to do small, surgical fixes.

The wrong choice is using Cursor as your unattended implementer stage. It's built around a human watching the editor; automating it end-to-end fights the tool. Conversely, the wrong choice is using Claude Code for quick exploratory edits where you want to see every keystroke, since its default posture is "make the change," not "suggest and wait."

What about GitHub Copilot and other agents in the pipeline?

GitHub's Copilot Workspace and Copilot coding agent (available on GitHub.com, documented at GitHub's Copilot docs) are worth a slot specifically for the PR-review stage, because they run inside GitHub's actual pull request UI and can comment directly on lines, which your other agents often can't do without extra tooling. I use Copilot's PR review as a second opinion after my custom reviewer agent, mostly because it has different training and different blind spots. It catches a different 10 percent of issues than a Claude-based reviewer does, which is the entire point of running more than one model.

OpenAI's Codex CLI and API-based agents are a viable implementer alternative to Claude Code, and in my experience GPT-5-class models are slightly better at large refactors that touch many files with a consistent pattern (renaming, adding a parameter everywhere), while Claude models tend to write cleaner, more idiomatic code on greenfield features. That's a qualitative impression from repeated use, not a benchmark claim, and it shifts with every model release, so re-validate it against your own codebase before betting a pipeline stage on it.

Cost and latency: the part nobody puts in the demo

A four-stage pipeline is expensive compared to a single chat session. Planning a nontrivial ticket might consume 15,000 to 40,000 tokens of context once you include relevant file contents. Implementation, especially with tool calls and file reads inside Claude Code, can run 100,000+ tokens for a multi-file change because every tool result gets appended to context. Review adds another full pass. On Claude's API pricing as of late 2025, a single non-trivial ticket through this pipeline can cost low single-digit dollars, and that adds up fast if you run it on every commit rather than every PR.

Latency is the other cost. A planner call takes 10 to 30 seconds. Implementation with multiple tool-call rounds can run 3 to 10 minutes for anything beyond a one-file fix. The deterministic gate (build plus tests) adds whatever your CI already takes, often another 2 to 8 minutes. End to end, a ticket that a competent engineer could hand-edit in 15 minutes might take 20 minutes through the pipeline, with the tradeoff being that it happens without you watching it, so you can run several in parallel on separate branches.

Failure modes worth planning for

The most common failure isn't the agent writing bad code. It's the agent writing code that passes its own tests because it wrote weak tests. A reviewer stage that only checks "do tests pass" is useless if the implementer wrote the tests. Mitigate this by having the planner stage write test specifications independently, before implementation starts, or by requiring the implementer to write tests first and having the reviewer verify the tests would fail against the old code (a real red-green check, not just presence of test files).

The second failure mode is context poisoning across stages. If the reviewer's context includes the implementer's justification text, it tends to agree with it rather than evaluate the diff cold. Strip commentary and pass only the diff plus the original plan to the reviewer stage.

The third is silent scope reduction: an agent that can't figure out how to do part of a task will sometimes quietly skip it and report success. This is the one that costs the most in production, because it doesn't fail loudly. The gate script needs to check "done_when" conditions from the plan explicitly, not just "did the process exit zero."

Putting it together: a workflow you can actually run this week

Start small. Take one recurring ticket type in your backlog (dependency bumps with test fixes, or CRUD endpoint scaffolding, work well) and wire up just two stages: an implementer (Claude Code or Cursor agent mode, run manually at first) and a deterministic gate script that runs your existing test suite. Don't add the planner or reviewer stage until you've watched the two-stage version fail a few times and you understand its specific failure pattern in your codebase.

Once you add a planner, make its output a file that lives in the repo (a scratch PLAN.md per ticket, deleted after merge) rather than something that only exists in an API response. That gives you a paper trail when the implementer deviates and you need to debug why.

Add the reviewer stage last, and run it as a second opinion alongside a human reviewer for the first few weeks, not as a replacement for one. Track how often it agrees with your human reviewers versus how often it flags something real that a human missed. If it's not catching anything a competent human wouldn't catch within a month of real use, the added cost and latency aren't worth it for that ticket type, and you're better off going back to a single well-scoped Claude Code or Cursor session with a human doing the review.