Everyone's building AI agents now. The hard part isn't getting one agent to work—it's getting multiple agents to work together without creating a distributed debugging nightmare.

This guide covers the engineering reality of multi-agent orchestration: when to use it, how to architect it, and the specific patterns that separate production systems from demos that break under load.

When Multi-Agent Actually Makes Sense

Single-agent systems are simpler. Always start there. Multi-agent architectures make sense when:

1. Task decomposition provides clear boundaries
Research agent + execution agent is clean. Three agents that all "help with planning" is architecture astronautics.

2. Parallel execution saves meaningful time
If your agents wait on each other sequentially, you've just added complexity for no gain.

3. Specialization improves accuracy
A code review agent that only reviews code will outperform a general agent doing code review as one of twenty tasks.

4. Failure isolation matters
When one subsystem failing shouldn't kill the whole workflow, separate agents with independent error boundaries make sense.

If your use case doesn't hit at least two of these, stick with a single agent that calls different tools.

The Four Core Orchestration Patterns

Pattern 1: Hierarchical (Boss-Worker)

One coordinator agent delegates to specialist agents. The coordinator doesn't do work—it routes tasks and synthesizes results.

When to use it:

• Complex workflows with clear task boundaries
• When you need central state management
• Customer-facing systems where one "face" improves UX

The catch: The coordinator becomes a bottleneck. Every decision flows through it. For high-throughput systems, this doesn't scale.

Pattern 2: Peer-to-Peer (Collaborative)

Agents communicate directly without a central coordinator. Each agent can initiate communication with others.

When to use it:

• Dynamic workflows where the next step isn't predetermined
• When agents need to negotiate or debate
• Research/analysis tasks with emergent structure

The catch: Coordination overhead explodes. You need robust message routing, timeout handling, and conflict resolution.

Pattern 3: Pipeline (Sequential Processing)

Each agent performs one stage of a linear workflow. Output from agent N becomes input to agent N+1.

When to use it:

• Clear sequential dependencies
• Each stage has distinct expertise requirements
• Quality gates between stages (review, validation, approval)

The catch: One slow stage blocks everything downstream. No parallelization.

Pattern 4: Blackboard (Shared State)

All agents read from and write to a shared state space. No direct agent-to-agent communication. The blackboard coordinates.

When to use it:

• Problems that require incremental refinement
• Multiple agents can contribute partial solutions
• Order of contributions doesn't matter
• Agents work asynchronously at different speeds

The catch: Race conditions and conflicting updates. Without careful locking, agents overwrite each other.

State Management: The Real Challenge

Multi-agent systems fail because of state management, not LLM capabilities. Here's how to do it right.

Distributed State Store

Don't store state in agent memory. Use Redis, DynamoDB, or another distributed store.

Event Sourcing for Audit Trails

Store every state change as an event. Reconstruct current state by replaying events.

Error Handling: Assume Everything Fails

Your agents will fail. Plan for it.

Retry Logic with Exponential Backoff

Implement retry mechanisms that progressively increase wait times between attempts.

Circuit Breaker Pattern

Stop calling a failing agent before it brings down the whole system.

Graceful Degradation

When an agent fails, fall back to a simpler alternative.

Monitoring and Observability

You can't debug what you can't see. Implement structured logging, distributed tracing, and key metrics for production systems.

Production Checklist

Before deploying multi-agent systems, ensure proper architecture, state management, error handling, and observability are in place.

When to Use Each Pattern

Hierarchical: Customer-facing chatbots, task automation platforms, any system with clear workflow stages.

Peer-to-peer: Research systems, collaborative problem-solving, creative content generation where structure emerges.

Pipeline: Data processing, content moderation, multi-stage verification workflows.

Blackboard: Complex planning problems, systems where order of operations doesn't matter, incremental refinement tasks.

The Bottom Line

Multi-agent systems aren't inherently better than single agents. They're different—trading simplicity for capabilities you can't get any other way.

Start simple. Add complexity only when it solves a real problem. And when you do go multi-agent, treat it like any other distributed system: assume failures, observe everything, and design for recovery.

The hard part isn't the agents. It's the engineering around them.