Multi-agent AI coding: planner, builder, reviewer

Multi-agent AI coding assistants split a request across specialised agents: a planner writes tasks with acceptance criteria, builders implement them, and a reviewer checks the output before returning it to you. The approach can produce better results than a single agent because each role focuses on one part of the pipeline, but it uses more tokens and takes longer because intermediate steps pass context between agents.

Why split the work

A planner agent reads your request and breaks it down into discrete tasks, each with an owner and clear acceptance criteria. This forces structure. Instead of a single agent trying to hold requirements, implementation and testing in one context window, the planner writes a specification that other agents execute against. When a builder finishes a task, the reviewer checks it against the criteria the planner set, catches mistakes early, and either approves the work or sends it back with specific notes.

In GroutCode Team mode, you choose a preset from the composer's mode menu: Engineering team, Planning team or Design team. The team lead agent breaks your request into tasks with owners and acceptance criteria, then hands them to specialist and reviewer agents that run in parallel. Each role can have its own preferred model and its own allowed tools. Specialist agents can escalate or ask each other questions mid-run, so a builder that hits an ambiguity can clarify with the planner without waiting for you.

The same pattern appears in GroutApp when you work with the 3D design studio or the circuit board tool. Both run visible teams. The 3D studio uses a Planner, Builder and Reviewer. The circuit board studio uses a Planner, multiple Designer agents and an Integrator. Each agent gets a card showing its status, current step, elapsed time and token count. You can send a message to the team while it runs, and agents will see it and adjust.

Where it wastes tokens

Every handoff repeats context. The planner writes tasks and sends them to builders. Each builder receives the plan, the relevant task and enough background to work. When a builder finishes, the reviewer receives the plan, the task, the builder's output and the acceptance criteria. If the reviewer rejects the work, the builder gets all of that context again plus the rejection notes. A complex request that produces three tasks and two rejection cycles can burn ten times the tokens a single agent would use.

Parallel execution helps, but it does not eliminate the cost. Running three builders at once is faster than running them in sequence, but all three still consume overlapping context. The team lead's plan appears in every builder's prompt. The final integration step, where a reviewer or integrator checks that all the pieces fit, adds another full-context pass.

GroutCode includes a token saver setting that trims what is sent to the model without changing the stored chat. It removes redundant background and compresses repeated references, but it cannot remove the structural cost of handing tasks between agents. If tokens matter more than quality, a single coding agent working through a request will always be cheaper.

When to use teams

Multi-agent mode makes sense for requests that need planning rigour. If you are building a feature with several moving parts, or refactoring a module with side effects in three other files, the planner's task breakdown keeps work organised and the reviewer catches integration problems before you see them. Single-agent mode is faster and cheaper for small fixes, one-file changes or exploratory work where you want to steer as you go.

The visible agent cards in GroutApp's 3D and circuit board studios show why teams work well for design tasks. You watch the planner interpret your sketch or your board requirements, see each builder or designer work on a subsystem, and track the integrator as it checks clearances or ensures mechanical fit. If something looks wrong halfway through, you send a message and the agents adjust. That visibility and mid-run control justify the token cost for complex designs.

Choosing models per role

GroutCode Team mode lets you assign a different model to each role. You might give the planner a slower, more capable model because the plan shapes everything downstream, then assign faster models to builders and the reviewer. A typical setup uses a reasoning model for the planner, a coding-specialist model for builders, and a general model for the reviewer. The flexibility matters because planner mistakes cascade and builder speed matters when you have three running in parallel.

In the circuit board studio, giving Designer agents access to layout tools but restricting the Planner to analysis tools prevents the planner from making implementation decisions too early. Role-specific tooling keeps responsibilities clear and reduces the chance that an agent will overstep and confuse the next agent in the chain.

Learning from agent work

If you are a student, treat multi-agent output as a teaching tool, not a shortcut. Read the planner's task breakdown and understand why it structured the work that way. Review the diff each builder produces and make sure you can explain every change. Do not hand in agent-generated code you cannot defend in a viva or adapt when requirements shift.

The token cost of multi-agent mode also teaches resource trade-offs. When you see that a three-agent request used fifteen times the tokens of a single-agent attempt, you learn to decide whether the quality gain justifies the cost. That decision, planning a task breakdown and reading a code review, are all skills that transfer to working with human teams.

Limits

Multi-agent systems add latency. Even with parallel execution, the planner runs first, then builders, then the reviewer. A request that a single agent completes in twenty seconds might take a minute with a team. If you need a quick answer or you are iterating rapidly, single-agent mode is faster.

Agent coordination is not perfect. A builder might misinterpret a task, the reviewer might miss a subtle bug, or the integrator might approve components that conflict when combined. You still read the output, test it and fix what breaks. Multi-agent mode improves average quality, but it does not eliminate your responsibility to understand and verify the result.