Planner and Worker Agents: How to Split Large AI Projects Without Losing Control

Large AI projects fall apart in a single conversation: forgotten decisions, inconsistent work, wasted tokens. Learn how a planner agent, scoped worker agents, and a shared decisions document keep a bigger project coherent.

Quick Answer

A planner agent understands the overall goal, breaks it into a tree of smaller tasks, and decides which model handles each piece. A worker agent completes one of those scoped tasks and reports back with evidence. Splitting a large AI project this way, instead of asking one agent to plan, execute, remember, and review everything in a single long conversation, avoids the context overload, inconsistency, and wasted effort that tend to sink big projects run in one continuous session. The pieces that actually make this work are a clear task breakdown, narrow worker boundaries, a shared decisions document everyone reads and updates, and review from two separate angles once the work is done.

Why One Agent Struggles With Large Projects

A single long conversation trying to carry an entire project runs into the same handful of problems, over and over. Context gets overloaded as the task grows, and the agent starts losing track of earlier decisions. Long conversations degrade: instructions given early get diluted by everything that came after them. Decisions made ten exchanges ago get silently forgotten or contradicted five exchanges later. Implementation becomes inconsistent, one part of the project follows a pattern established early, another part quietly drifts from it. Tokens get spent redundantly, the agent re-reads and re-reasons about the same context repeatedly instead of working from a settled plan. The same categories of mistakes repeat because nothing captured the lesson from the first time. Review becomes weak, because there’s no natural checkpoint where a fresh set of eyes, human or AI, looks at the work independently. And scope creeps without anyone deciding it should, because there was never a boundary defining what’s actually in scope.

None of this is really about model capability. It’s a structural problem: one continuous context is a bad fit for a project with more moving parts than it can reliably hold.

What a Planner Agent Does

The planner’s job is to understand the goal well enough to break it apart correctly. That means identifying dependencies between pieces of work (what has to happen before what), creating a task tree that breaks the project into discrete, assignable pieces, defining clear boundaries for each task so workers aren’t guessing at scope, setting concrete success criteria so it’s obvious when a task is actually done, choosing which model or effort level fits each task, and flagging risks up front rather than letting a worker discover them mid-task.

A planner’s mistakes are the expensive ones. A missed dependency, an ambiguous task boundary, or a vague success criterion doesn’t just affect the planner, it propagates into every worker that relies on it. This is the main reason planning is usually worth your strongest available model, even when most of the actual execution runs on something cheaper.

What a Worker Agent Does

A worker’s job is narrower and, done well, much more mechanical: complete one scoped task, follow the shared rules set up for the project, report back with evidence rather than just a claim of completion, log anything it discovers that other workers or the planner should know about, escalate to the planner or a human when it hits a real blocker, and avoid touching anything outside its assigned scope, even if it notices something else that looks worth fixing.

That last point matters more than it sounds. A worker that “helpfully” fixes something adjacent to its task is a worker that just made an unreviewed, unplanned change, which is exactly the kind of drift a planner-worker split is supposed to prevent.

What an Agent Swarm Is

An agent swarm is a group of AI agents collaborating on different parts of a larger goal, and it’s worth introducing here because it’s easy to picture as something bigger and more exotic than it usually is in practice. A swarm doesn’t need to mean dozens of autonomous agents running independently. A small, genuinely useful swarm might be exactly what this guide describes: one planner, a handful of workers, and one reviewer, coordinated through a shared plan rather than each agent freelancing on its own understanding of the goal.

Build a Task Tree

Breaking a project into a task tree usually follows a natural shape: research (understanding the existing system, gathering requirements, or reviewing prior art), architecture (deciding the overall approach before implementation starts), implementation (the actual build, ideally split into independent or clearly sequenced pieces), testing (verifying the implementation actually works), documentation (capturing what was built and why), and review (checking both the output and the process that produced it).

Not every project needs all six branches, and they don’t have to run strictly in that order. But naming them explicitly, rather than letting “implementation” quietly absorb research, testing, and review, is what keeps a task tree from becoming a to-do list with no real structure behind it.

Create a Shared Decisions Document

This is the piece that keeps a multi-agent project from repeating its own mistakes. A shared decisions document is a living file every agent reads before starting and updates as it works. A useful version includes:

• Goal: what the project is actually trying to achieve, in plain language
• Architecture: the agreed-on approach, so workers aren’t each inventing their own
• Constraints: hard requirements, things that must not change, technical limits
• Naming rules: conventions for files, functions, or content, so output stays consistent across workers
• Completed decisions: choices already made, with enough context that they don’t get silently revisited and reversed
• Unresolved questions: open items that need a decision before certain work can proceed
• Discovered pitfalls: mistakes or dead ends a worker already hit, so the next one doesn’t repeat them
• File or module ownership: who’s responsible for what, to prevent conflicting edits
• Current status: a snapshot of what’s done, in progress, and blocked

This document is what lets a worker on task twelve benefit from something a worker on task three learned, without either of them sharing a conversation history.

Define Worker Boundaries

Vague instructions are the most common way a multi-agent workflow goes sideways. A worker’s assignment should include the outcome it’s responsible for, exactly which files or areas it’s allowed to touch, what it’s explicitly forbidden from changing, what it depends on from other tasks, concrete success criteria, how it should verify its own work, the format it should report back in, and clear stop conditions, what “I’m blocked, escalate this” looks like versus “keep trying.”

A worker with a tight, well-defined boundary is predictable. A worker with a loose one will eventually surprise you, usually not maliciously, just by filling the ambiguity with its own best guess.

Route Models by Task

Not every task in the tree deserves the same model. This is model routing applied across a whole project rather than a single prompt. A reasonable default: your strongest model for planning, since mistakes there are the most expensive. A cheaper or faster model for repetitive, well-scoped execution, where the planner has already done the hard thinking. A specialized model where the task calls for it, a strong coding model for implementation, a strong research-oriented model for the research branch. A fresh model, or at least a fresh context, for review, so the reviewer isn’t carrying the same blind spots as the agent that did the work. And human approval for anything sensitive, irreversible, or high-stakes, regardless of which model produced it. See Match the AI Agent to the Risk for the fuller framework on deciding effort and review level per task, and How to Estimate an AI Agent’s Cost for budgeting each piece before it starts.

Avoid Conflicting Work

Multiple agents touching a shared codebase or content set is where things break down fastest if it isn’t planned for. Assign clear file or module ownership so two workers aren’t editing the same thing simultaneously. Use separate branches or worktrees where your tooling supports it, so parallel work doesn’t collide until it’s ready to merge. Sequence genuinely dependent tasks rather than running them in parallel and hoping for the best. Keep the shared decisions document current so agents aren’t working from stale assumptions about what’s already been done. And merge deliberately, in a defined order, rather than accepting whichever worker finishes first without checking it against what else changed.

Review From Two Angles

Once a worker’s task is done, review it from two separate angles, not one. First, review the finished output itself: does it actually work, does it meet the stated success criteria, does it hold up under a real test rather than just looking plausible. Second, review the agent’s decisions and, where available, its transcript: what did it assume that wasn’t explicitly stated, what edge case did it skip, what would a human have wanted to weigh in on. The first review catches broken output. The second catches the quieter problem of technically-working output built on a bad assumption nobody caught. For a structured version of this kind of review, see The Red, Blue, Green AI Review Loop and Make AI Prove Its Work Before You Trust It.

When Not to Use a Swarm

A planner-worker setup isn’t the right tool for every task. Skip it when the task is small enough that a single agent can hold the whole thing in context comfortably. Skip it when coordination overhead, writing the plan, the shared document, the worker boundaries, would cost more time than it saves. Skip it when several agents would need to edit the same file, since that’s a conflict risk a swarm structure doesn’t automatically solve. Skip it when the architecture isn’t settled yet, since planning a task tree on top of an undecided foundation just locks in the wrong shape early. And skip it when there’s no reliable way to review the result, since a swarm without review is just several unmonitored agents instead of one.

Copy-Paste Planner Prompt

Act as the planner for this project: [describe the goal]. Break it into a
task tree covering research, architecture, implementation, testing,
documentation, and review as needed. For each task, define: what it depends
on, what "done" looks like, which model or effort level fits it, and any
risks worth flagging up front. Produce a shared-decisions document template
covering goal, architecture, constraints, naming rules, and file ownership.
Write a scoped worker prompt for each task, including allowed files,
forbidden changes, success criteria, and stop conditions. Propose a review
plan, and a budget or stop condition for the project as a whole.

Example Workflows

A website redesign might split into: research (current site audit, competitor review), architecture (information architecture, component system), implementation (one worker per major page or section), testing (cross-browser and responsive checks), and review (design consistency pass, content review). A research report might split into: source gathering, per-source summarization workers, a synthesis worker combining findings, and a review pass checking sourcing and claims. A content campaign might split into: strategy and calendar (planner-level), individual piece drafting (one worker per piece), and a consistency review across the whole set. A software feature might split the way the task tree above describes directly: research, architecture, implementation workers per component, tests, docs, and review. A knowledge base cleanup might split into: an audit worker cataloging what exists and what’s outdated, per-section rewrite workers, and a final consistency and linking review.

Final Takeaway

The number of agents involved matters less than most people assume. What actually determines whether a multi-agent project stays coherent is strong planning up front, narrow and well-defined worker scopes, a shared decisions document that keeps knowledge from evaporating between agents, and independent review once the work is done. Get those four things right, and a swarm of one planner and three workers can outperform a much larger, less structured setup. Get them wrong, and more agents just means more ways for the project to drift.

For the supervision side of running several agents at once without watching them constantly, see How to Supervise Multiple AI Agents Without Checking Them All Day. For matching effort level to task risk across the whole tree, see Match the AI Agent to the Risk.