Planning
Turn a broad request into bounded work
A planner maps the affected files, dependencies, tests, and acceptance criteria before implementation begins.
Multi-agent coding workflows
Plan, delegate, verify, and review software work with multiple AI coding agents.
Multi-agent coding is a workflow pattern for breaking one engineering request into scoped roles: planner, implementer, tester, reviewer, and synthesizer. The point is not to replace engineering judgment. It is to make AI-assisted work easier to inspect before it reaches a pull request.
What multi-agent coding means
In a single-agent workflow, one model often plans, writes, checks, and summarizes its own work in the same context. That can be useful for small edits, but it becomes fragile when the task has multiple surfaces: data models, APIs, UI states, release constraints, and tests.
Multi-agent coding separates those concerns. One agent can plan the work, another can implement a narrow change, another can look for missing tests, and another can review the result from a fresh angle. The workflow is valuable because the roles are explicit and the artifacts are easier to inspect.
The practical standard is simple: the agent that writes the code should not be the only agent that approves it.
Example task split: add billing settings
A multi-agent workflow should return more than a finished diff. It should show how the work was divided, which evidence was produced, and which pieces still need human judgment.
| Role | Owned work | Evidence expected |
|---|---|---|
| Planner | Define the settings resource, non-goals, acceptance criteria, and likely files. | Task graph, dependency order, and explicit questions. |
| Backend builder | Implement validation, persistence, API behavior, and migration impact. | Unit or integration tests covering writes, reads, invalid input, and permissions. |
| Frontend builder | Build the form, loading states, empty states, errors, and accessible labels. | Component checks, screenshots or preview notes, and state coverage. |
| Verifier | Run repository checks and inspect boundary behavior. | Commands run, failures found, skipped checks, and residual risk. |
| Reviewer | Inspect the combined diff for mismatched assumptions. | Merge recommendation, blocked items, and human decision points. |
If the output only says "implemented billing settings successfully," the workflow has failed as an engineering system. The value is in the split, the checks, and the rejected assumptions.
Where separate agents help
Implementation
Keep coding tasks narrow
Builder agents work best when each task has a clear owner, a small surface area, and a specific output to produce.
Verification
Measure code against reality
Verifier agents should prefer executable evidence: tests, type checks, linters, previews, and reproducible command output.
A practical multi-agent coding loop
Clarify the engineering request
Capture the goal, constraints, affected systems, and non-goals. Ambiguity should be surfaced early instead of hidden inside generated code.
Split the work by responsibility
Separate database changes, API behavior, UI states, tests, documentation, and release checks when the task is large enough to justify it.
Implement with local context
Each builder should receive the relevant files, expected behavior, and verification command for its slice of the work.
Verify before synthesis
Run the checks that the repository actually supports. When a check cannot run, the final summary should say that plainly.
Review the combined result
A reviewer looks for integration risks: inconsistent assumptions, missing migrations, unhandled empty states, weak validation, or thin test coverage.
What a good workflow does not claim
Multi-agent coding is not a benchmark score, a promise of autonomous shipping, or a guarantee that more agents always produce better code. More roles can add coordination cost if the task is small or the boundaries are unclear.
The useful claim is narrower: explicit roles make AI-generated work more reviewable. They create more opportunities to catch mismatched assumptions before a human engineer decides whether the change is ready.
Related Concertor reading
These articles go deeper on model diversity, cross-model review, and why verification needs more than model confidence.
FAQ
What is multi-agent coding?
Multi-agent coding is an AI-assisted software workflow where separate agents handle distinct responsibilities such as planning, implementation, testing, review, and synthesis.
Is multi-agent coding only useful for large projects?
No, but it is most useful when a request crosses multiple concerns. A small copy change may not need separate agents. A feature touching data, API behavior, UI states, and tests usually benefits from clearer role separation.
Does using more agents make code automatically correct?
No. Agents can still make mistakes. The value comes from scoped responsibilities, independent review, and executable verification rather than from agent count alone.
Where should humans stay involved?
Humans should own product intent, architecture tradeoffs, security-sensitive decisions, production release judgment, and any area where the evidence is incomplete or the models disagree.
One request, coordinated agent work.
Concertor is built around a simple operating principle for AI coding: plan the work, delegate it clearly, verify with evidence, and keep the reasoning trail visible.
Visit Concertor