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Multi-Agent Collaboration

What It Is

Multi-agent collaboration is a pattern where multiple AI agents — each with different roles, tools, or expertise — work together to accomplish a task. Instead of one general-purpose agent doing everything, the work is divided among specialists that communicate, delegate, and coordinate.

Think of it like a team at a company: a researcher gathers information, an analyst processes it, a writer drafts the report, and an editor reviews it. Each agent focuses on what it does best, and the combined output is better than any single agent could produce alone.

Why It Matters

Single agents hit a complexity ceiling. As tasks grow more complex, a single agent's context window fills up, its instructions become contradictory, and its performance degrades. Multi-agent systems solve this by decomposing the problem across agents that each operate with focused context and clear responsibilities.

Multi-agent collaboration also enables separation of concerns — a safety-critical agent can enforce policies while a creative agent generates content, without either interfering with the other's role.

How It Works

┌──────────────┐
│  Orchestrator │
│  (coordinator)│
└──┬───┬───┬───┘
   │   │   │
   ▼   ▼   ▼
┌────┐┌────┐┌────┐
│ A1 ││ A2 ││ A3 │
│    ││    ││    │
└────┘└────┘└────┘
Researcher  Analyst  Writer

Common architectures:

  • Orchestrator pattern — A central agent assigns tasks to specialist agents, collects results, and synthesizes the final output.
  • Pipeline pattern — Agents are arranged in a sequence where each agent's output becomes the next agent's input (researcher → analyst → writer).
  • Debate pattern — Two or more agents argue different perspectives, and a judge agent selects or synthesizes the best answer.
  • Peer collaboration — Agents communicate as equals, each contributing their expertise to a shared workspace.

Deep dive: Orchestration Patterns

For detailed coverage of each coordination topology — including selection guides, diagrams, and platform examples — see Orchestration Patterns.

The key design decisions are:

  1. How many agents? — Use the minimum number needed. More agents means more coordination overhead.
  2. How do they communicate? — Through shared context, message passing, or a shared workspace.
  3. Who decides what? — An orchestrator, a round-robin, or autonomous negotiation.

Example

Customer exchange scenario

A customer exchange can be handled by a team of agents:

  • Triage Agent — Reads the customer's request, classifies it as "exchange," and routes it to the right specialist.
  • Order Agent — Looks up the order, verifies return eligibility, and handles the logistics.
  • Inventory Agent — Checks stock levels and finds the best warehouse to ship from.
  • Communications Agent — Drafts the customer-facing email with exchange details and return instructions.

Each agent has access only to the tools it needs — the Communications Agent can't process payments, and the Order Agent can't send emails. This limits the blast radius of any single agent's mistakes.

Content production

A marketing team's content pipeline as a multi-agent system:

  • Research Agent — Gathers data, competitor analysis, and market trends
  • Writer Agent — Produces a draft based on the research brief
  • Editor Agent — Reviews for clarity, accuracy, and brand voice
  • SEO Agent — Optimizes headlines, metadata, and keyword placement

The Writer Agent never sees raw data — it receives a structured brief from the Research Agent. The Editor Agent doesn't know about SEO — it focuses purely on quality. This separation produces better results than a single agent trying to juggle all four concerns.

When to Use It

  • Tasks that require multiple distinct skill sets (research + writing + analysis)
  • Workflows where separation of concerns improves quality or safety
  • Problems where debate or multiple perspectives lead to better answers
  • Systems that need to scale — adding a new capability means adding a new agent, not rewriting the existing one
  • Production systems where different agents need different permission levels
  • Planning — An orchestrator agent often uses planning to coordinate the team
  • Tool Use — Each agent typically has its own set of tools
  • Reflection — A critic agent reviewing another agent's work is multi-agent reflection
  • Guardrails — Each agent can have its own guardrails, limiting what it can do
  • Agent Capability Patterns

Further Reading