Multi-Agent Patterns That Actually Work

"Let's use multiple AI agents working together!"

Usually, this is a solution looking for a problem. Single agents with good tools solve most use cases.

But sometimes — sometimes — multiple agents genuinely work better. Here are the patterns I've found valuable.

When NOT to Use Multi-Agent

First, the negative space.

Don't use multi-agent for:

• Simple request-response flows
• Tasks a single agent with tools can handle
• Impressive demos with no production requirement
• Adding complexity to seem sophisticated

Multi-agent systems are harder to debug, more expensive to run, and more complex to maintain. Only use them when the benefits outweigh these costs.

When Multi-Agent Makes Sense

Use multi-agent when:

• Different tasks require fundamentally different capabilities
• Tasks can run in parallel for speed
• You need human review at specific stages
• Specialization genuinely improves quality
• Error isolation matters (failure in one shouldn't cascade)

With that filter, here are patterns that actually work.

Pattern 1: Router/Dispatcher

The simplest multi-agent pattern. One agent routes to specialists.

User Query
    ↓
[Router Agent]
    ├─→ Sales Agent (if sales inquiry)
    ├─→ Support Agent (if support issue)
    └─→ Technical Agent (if technical question)

When to use: When you have distinct domains that need different instructions, tools, or knowledge bases.

Key insight: The router should be fast and cheap. Use a smaller model. Route based on clear signals.

Failure mode: Over-routing. If every query goes through a router before doing anything, you've added latency and cost for no benefit.

Pattern 2: Parallel Execution

Multiple agents work simultaneously, results get merged.

Research Query
    ↓
[Coordinator]
    ├─→ [Web Research Agent]
    ├─→ [Database Query Agent]
    └─→ [Document Search Agent]
    ↓ (all complete)
[Synthesizer Agent]
    ↓
Final Answer

When to use: When you need information from multiple sources and can query them concurrently.

Key insight: Parallelism only helps if the subtasks are truly independent. If Agent B needs Agent A's output, it's not parallelism.

Failure mode: Fan-out explosion. Spawning 20 parallel agents for a simple query is wasteful. Limit concurrency.

Pattern 3: Hierarchical (Supervisor-Worker)

A supervisor decomposes tasks, assigns to workers, aggregates results.

Complex Task
    ↓
[Supervisor Agent]
    ↓ (breaks into subtasks)
    ├─→ [Worker A] → subtask 1
    ├─→ [Worker B] → subtask 2
    └─→ [Worker C] → subtask 3
    ↓ (workers complete)
[Supervisor Agent]
    ↓ (aggregates and refines)
Final Output

When to use: Complex tasks that naturally decompose. When you need oversight over specialist work.

Key insight: The supervisor needs to be genuinely smarter/more capable than workers. Otherwise, why have hierarchy?

Failure mode: Over-decomposition. Breaking simple tasks into subtasks adds overhead. Let the supervisor handle simple things directly.

Pattern 4: Critic-Refiner

One agent generates, another critiques, iterate until good.

Task
    ↓
[Generator Agent]
    ↓
Draft Output
    ↓
[Critic Agent]
    ↓ (if not good enough)
Feedback → Generator → Draft → Critic → ... (loop)
    ↓ (if good enough)
Final Output

When to use: When quality matters more than speed. Code generation. Writing. Analysis.

Key insight: The critic must have clear criteria. "Is this good?" is useless. "Does this handle edge case X?" is useful.

Failure mode: Infinite loops. Always have a max iteration limit. Sometimes "good enough" after 3 iterations beats "perfect" after 10.

Pattern 5: Human-in-the-Loop

Agent proposes, human approves, agent continues.

Request
    ↓
[Agent proposes action]
    ↓
[Human Review Gate]
    ├─ Approved → Execute → Continue
    └─ Rejected → Agent revises → Human Review → ...

When to use: High-stakes decisions. Actions with real-world consequences. Anything involving money, customer communication, irreversible changes.

Key insight: The human gate should be at decision points, not everywhere. Too many approvals = approval fatigue = rubber stamping.

Failure mode: Blocking too often. If humans approve 99% automatically, the gate is probably in the wrong place.

Pattern 6: Debate/Committee

Multiple agents independently assess, then reconcile.

Decision Needed
    ↓
[Agent A: Perspective 1]
[Agent B: Perspective 2]
[Agent C: Perspective 3]
    ↓
[Judge Agent]
    ↓
Final Decision (with reasoning)

When to use: Decisions where diverse perspectives genuinely help. Risk assessment. Ethical considerations. Complex tradeoffs.

Key insight: The perspectives must actually differ. Same model three times isn't debate. Different instructions, different priors, different emphases.

Failure mode: Artificial disagreement. If agents always agree, you don't need a committee. If they always disagree, the judge is just picking randomly.

Pattern 7: Pipeline (Sequential Handoffs)

Each agent handles one stage, passes to the next.

Raw Input
    ↓
[Extraction Agent] → Structured Data
    ↓
[Validation Agent] → Verified Data
    ↓
[Enrichment Agent] → Enhanced Data
    ↓
[Output Agent] → Final Response

When to use: When stages are truly sequential and benefit from specialization. Data processing. Document workflows.

Key insight: Each stage should add clear value. If a stage is just "check and pass through," eliminate it.

Failure mode: Too many stages. Each handoff adds latency and potential for error. Fewer stages is usually better.

Implementation Notes

1. Clear Contracts

Each agent should have:

• Defined input format
• Defined output format
• Clear success/failure criteria

Without contracts, agents can't reliably communicate.

2. Error Isolation

One agent failing shouldn't crash the whole system. Handle failures gracefully:

• Timeouts per agent
• Fallback behaviors
• Clear error reporting

3. Cost Tracking

Multi-agent = multi-cost. Track spending per agent, per pattern, per execution. Optimize the expensive parts. (See The Cost Problem in AI Nobody Talks About for more on this.)

4. Observability

Debugging multi-agent systems is hard. Log everything (see A Manifest for Better Logging):

• Which agent did what
• What was passed between agents
• Where time was spent
• What decisions were made

5. Start Simple

Begin with a single agent. Add complexity only when you have evidence it helps.

Most systems I've seen start too complex. The best multi-agent systems evolved from single-agent systems that hit clear limitations.

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Multi-agent systems aren't magic. They're architecture. Use them when the architecture genuinely serves the problem. Otherwise, a good single agent with the right tools will serve you better.