Five ways to coordinate multiple AI agents, from the simplest handoff to fully autonomous teams. Multi-agent designs rarely improve raw intelligence. They help with scaling effort, structuring work, or adding checks, and they often increase cost and latency. Each pattern has a sweet spot. The goal is knowing when to reach for which one, and when a single well-prompted agent is still the better answer.
Simpler to set upMore moving parts
01
The Relay
Like passing a file between departments
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How It Works
Agent A completes a task and its output becomes the input for Agent B. You control the handoff. Each agent has a focused role and a narrow system prompt. The output moves in one direction.
Best For
Linear workflows where each step is distinct. Draft, then edit, then format. Research, then summarize, then translate. Any process where one job finishes before the next begins.
Most workflows should start with one agent. Only when you consistently need a different persona or toolset for step two should you introduce a relay. A single well-prompted agent handles most tasks. This pattern is for the moment when one context window is not enough, not before.
02
The Supervisor
Like a project manager assigning tasks to specialists
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How It Works
One orchestrator agent receives the full task, breaks it into subtasks, and dispatches those to specialized worker agents. The orchestrator collects results and synthesizes a final output. It decides when the work is done.
Best For
Complex tasks that can be decomposed. Research where multiple angles need coverage simultaneously. Any job where a generalist directing specialists outperforms a single generalist working alone.
The quality of the orchestrator's task decomposition determines everything. A bad breakdown produces bad results regardless of how capable the workers are. This mirrors real management: delegation skill matters more than team size. The main failure mode is bad task decomposition. Only use this pattern when you already understand the task well enough to write a decent manual process for it. If you cannot describe the steps yourself, an orchestrator cannot delegate them.
03
The Debate
Like red team / blue team exercises
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How It Works
Two agents with opposing directives review each other's work iteratively. One generates, the other critiques. The output passes back and forth for a set number of rounds or until a quality threshold is met.
Best For
Stress-testing arguments, improving writing quality, catching logical gaps. Any situation where adversarial pressure improves the output. This is Andy's "now be a skeptic" approach, automated across two agents.
Proposal reviewRisk analysisStrategic planning
Key Insight
This pattern fights confirmation bias structurally. A single AI tends to agree with its own prior output. Two agents with opposing mandates force the kind of constructive friction that improves decisions. You already do this instinctively when you ask Claude to argue against your position. One caveat: evidence for large accuracy gains from agent-vs-agent debate is mixed. Use this for high-stakes judgment calls and contentious proposals, not every email or memo.
04
The Shared Workspace
Like a team editing the same document from different angles
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How It Works
Multiple agents read from and write to shared resources: a database, a file system, a CRM. Each agent has its own role and toolset but operates on the same artifacts. Coordination happens through the shared state, not through direct conversation.
Best For
Workflows where different expertise needs to touch the same deliverable. One agent pulls data, another formats it, a third audits for compliance. This is closest to how real cross-functional teams operate.
CRM enrichmentCompliance reviewData pipelines
Key Insight
This is where MCP (Model Context Protocol) becomes relevant. It gives AI agents the ability to read and write to external tools: your calendar, your CRM, your file system. The agents do not talk to each other directly. They coordinate through the artifacts they share, the same way a real team coordinates through shared documents and systems.
05
The Autonomous Team
Like a self-managing squad with defined roles and a shared mission
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How It Works
Multiple agents operate with high autonomy, making decisions about what to do next, when to escalate, and how to divide work. A framework manages routing, state, and communication between agents. Human involvement is limited to setting the goal and reviewing the result.
Best For
Large-scale or recurring workflows where the cost of human orchestration exceeds the risk of autonomous decision-making. Customer support triage, continuous monitoring, complex research synthesis. This is the frontier, and most teams are not here yet.
Support triageContinuous monitoringComplex research
Key Insight
Frameworks like CrewAI and LangGraph make this possible today, but the debugging and cost overhead is real. The right question is not "can I build an autonomous team?" but "does the value of removing myself from the loop exceed the cost of the mistakes the team will make without me?" For most executive workflows right now, the answer is not yet. Before you consider this pattern, confirm you have: clear KPIs for what success looks like, robust monitoring that catches failures fast, rollback paths for when it goes wrong, and organizational sign-off for AI-initiated actions.
