Agent Orchestration Patterns: When to Chain, Fan-Out, or Delegate

Most AI agent discussions focus on what a single agent can do. But the real power — and the real complexity — emerges when you coordinate multiple agents to tackle workflows that no single agent could handle alone.

We've identified three fundamental orchestration patterns that cover 90% of the multi-agent architectures we build. Here's when to use each one.

Pattern 1: Chain (Sequential)

Agents execute in sequence. The output of Agent A becomes the input to Agent B.

Best for: Linear workflows where each step depends on the previous one — document processing pipelines, multi-stage data enrichment, approval workflows.

Example: An insurance claims pipeline where Agent 1 extracts data from the claim form, Agent 2 cross-references policy details, Agent 3 flags anomalies, and Agent 4 drafts a response.

Watch out for: Error propagation. If Agent 1 hallucinates the wrong claim amount, everything downstream is wrong. Build validation gates between agents.

Pattern 2: Fan-Out (Parallel)

One coordinator agent delegates to multiple specialist agents simultaneously, then aggregates their outputs.

Best for: Research tasks, multi-source analysis, anything where you need multiple perspectives or data sources analyzed independently.

Example: A competitive analysis agent that fans out to: Agent A (pricing research), Agent B (product feature comparison), Agent C (customer sentiment analysis), and Agent D (market positioning). The coordinator synthesizes their findings into a single report.

Watch out for: Cost multiplication. Fanning out to 5 agents that each make 3 LLM calls is 15 calls per task. Budget accordingly.

Pattern 3: Delegate (Hierarchical)

A supervisor agent breaks a complex task into sub-tasks and delegates each to the most appropriate specialist — potentially with sub-delegation.

Best for: Complex, multi-domain workflows where different parts require different expertise — enterprise operations, multi-department workflows, complex customer service.

Example: An operations agent that receives "resolve this customer escalation." It delegates: fact-finding to a data agent, policy review to a compliance agent, response drafting to a communication agent, and final review back to the supervisor.

Watch out for: Supervisor bottleneck. If the supervisor agent is a single point of failure (or cost), your system isn't truly scalable. Consider multiple supervisor instances or state-machine-based routing.

Which pattern for which problem?

• Is your workflow a pipeline? → Chain
• Do you need multiple independent analyses? → Fan-Out
• Is the task complex with many sub-domains? → Delegate
• All of the above? → You probably need a hybrid — and a good architecture review

Production considerations

• Observability: Multi-agent systems are harder to debug. Every agent-to-agent handoff needs logging.
• Timeouts: Fan-out is only as fast as your slowest agent. Set per-agent timeouts and have fallback strategies.
• Cost tracking: Tag every LLM call with the agent, pattern, and task ID. You'll need this data to optimize.

The bottom line

Multi-agent orchestration isn't about creating a "society of AI minds." It's about decomposing a complex workflow into manageable, testable, monitorable pieces. Pick the simplest pattern that solves your problem. Add complexity only when you've proven the simpler approach isn't enough.