Multi-agent AI coding: an orchestration and delegation playbook
Most people run one AI coding assistant and point it at a task. That works, but it leaves the biggest gains on the table. The real unlock in multi-agent AI coding is running several agents as a coordinated team, each with a clear job: an orchestrator that plans and owns anything risky, cheaper subagents it delegates to so its own context stays lean, an independent reviewer on a different model that catches what the main one is blind to, and a low-cost worker for the grunt tasks. This is the orchestration and delegation playbook I use, the routing rule that keeps it affordable, and the one piece that turned out to matter most.
Start with one orchestrator that owns the risk
The orchestrator is your main, strongest model, and it stays in charge. It plans the work, writes the correctness-critical code, and owns anything hard to undo: commits, merges, and deploys. Everything else in the system reports to it, and nothing else is allowed to take an outward, irreversible action on its own. This single rule is what keeps a fleet of agents from turning into chaos. You always have one place where the whole change is understood and one hand on the wheel. The other agents make the orchestrator faster and sharper, but they never replace its judgment or its authority over the repository.
Delegate inward first: subagents for token efficiency
Before reaching for outside tools, the orchestrator should delegate inside its own model. It does not need to do everything in its expensive main context. Instead it spins up subagents, cheaper and faster model tiers, running in parallel, each in a separate context window. When a subagent goes off to read forty files or run a wide search, all of that reading happens in its context, not the main thread. What comes back is the conclusion, not the pile of files it waded through.
This is the quiet token-efficiency win that most people skip. Your premium model spends its tokens on judgment, not on grunt reading, and its main context stays lean, which keeps its reasoning sharp. Because the bulk of the fanned-out work runs on small, cheap models, a parallel run that sounds extravagant usually costs less than doing it all inline on the expensive model. The rule is to pin the model per job rather than letting everything inherit the biggest one. Cheap workers for scanning and searching, the heavyweight thinker only where the problem earns it.
Add an independent reviewer on a different model
The highest-leverage agent in the whole setup is an independent reviewer running a different model family from your orchestrator. It reviews every meaningful change before it ships. The reason it works is not that it is smarter, it is that it is different. A model reasons in its own grooves and shares its own blind spots with itself, so a model reviewing its own work tends to miss the same things twice. A reviewer built on a different foundation, for example pairing a Claude-based orchestrator with a reviewer running a different frontier model, fails in different places. Where your main model is blind, the other one often sees clearly.
Treat its output as advice, not law. The reviewer hands back findings; the orchestrator verifies each one against the actual code, keeps the real ones, and pushes back on the wrong ones. The reviewer finds problems, the orchestrator fixes them, and you stay the one integrator with a coherent view of the change. That separation between who finds and who fixes is what keeps the review honest and the codebase consistent.
Hand grunt work to a cheap, sandboxed worker
The third role is a cheap worker, often a small or free-tier model, for low-risk work whose output is easy to check: drafts, summaries, scans, boilerplate, first passes at documentation. It is fast and nearly free, and that is exactly why it must be sandboxed. A weak model let loose with real permissions is a liability, so the worker runs in an isolated copy of the project with restricted access and a tightly scoped toolset. It can produce useful work, and it cannot touch anything that matters. You review what it hands back before any of it merges.
The discipline here is knowing what not to send it. Correctness-critical code, security, anything on the money path, and anything that reaches outside the machine stay with the orchestrator. If a task makes you wish the cheap worker were a bit smarter, that is the signal it was mis-routed, and it should go to a stronger agent instead. Pushing a weak model past its competence just moves the cost from the model to the rework.
The routing rule that keeps it cheap
One rule ties the whole system together: route each task to the cheapest engine that can do it correctly, where the cost also counts the time to set the task up and to check the result. The trap in multi-agent work is treating delegation as free. It is not. Handing off a task and verifying what comes back has real overhead, and for a small, tightly scoped change, doing it yourself in the main thread is faster and cheaper than the ceremony of dispatching an agent. Never delegate what is quicker to just do.
The economics get better when you notice that different engines bill on different meters. A reviewer or worker running on a separate subscription or a free tier does not draw down the same budget as your main model, so moving suitable work to them lowers the metered bill outright. The catch is that the saving only holds if their output is bounded and easy to check. If verifying a delegated result means pulling a thousand lines of noise back into your expensive main context, you have spent more than you saved. Capture the output to the side, read only the conclusion, and keep the premium context clean.
Why the independent reviewer is the highest-leverage piece
If you adopt only one part of this, make it the different-model reviewer. On a recent build, it flagged a bug that had already shipped to production, one my main model's own review had waved straight through. Then, reviewing the fix for that bug, it caught a fresh mistake I had introduced in the correction itself. Two catches, both on work my main model had already looked at and approved. That is the whole argument in one story: a second, uncorrelated mind sees what the first cannot, and it is cheapest to catch those misses before they ship rather than after. One model checking its own homework is not a review. Two different models is.
The honest limits
Fanning out is not free and not always right. Every agent costs tokens, coordination has overhead, and agents editing the same files in parallel will collide if you let them. A slow, thorough reviewer is worth backgrounding while you keep working, not something to sit and wait on. And for a small, linear task, one focused session beats the ceremony of standing up a team. The skill is reading the shape of the work in front of you: genuinely independent problems reward a fleet, while one tightly coupled thread of changes wants a single pair of hands. Reach for the team when the work is actually wide, not by reflex.
The bottom line
Multi-agent AI coding is less about having more models and more about giving each a clear job and a clear boundary. An orchestrator that owns the risk, subagents it delegates to so its context stays lean and cheap, an independent reviewer on a different model that catches its blind spots, and a sandboxed worker for the grunt tasks. Route each task to the cheapest engine that can do it right, keep the reviewer independent, and keep yourself as the integrator. It builds on the base setup and its guardrails, extends running agents as a team, and pairs with skills the agents can run and a shared memory they draw on.
If you want AI agents wired into your business this way, doing real work with a review gate that actually catches mistakes, send a briefand I'll scope it with you.