Agent Orchestration: Coordinating Multiple Agents

Series: Claude Learning Journey · Expert

Multi-agent systems are programs where the components are not functions or objects but AI agents — entities that can reason, decide, and act. Orchestration is the discipline of coordinating those agents: making sure they share context appropriately, that their outputs are combined correctly, and that failures in one agent do not cascade into system failures.

This is a different kind of software engineering. The components do not behave deterministically. They require different testing, different error handling, and different trust models than traditional code. This post is about thinking correctly about orchestration and where it makes sense.

What Orchestration Actually Means

Orchestration is not just running multiple agents. It is defining the relationships between them: who decides what work to assign, how agents share context, how their outputs are combined, and how failures are handled.

The simplest orchestration is a pipeline: agent A does something, passes the result to agent B, which passes to agent C. This is linear and easy to reason about. More complex orchestration involves agents that work in parallel, agents that can call other agents, and agents that make decisions about how to proceed based on intermediate results.

Where Orchestration Makes Sense

Orchestration makes sense when:

• The task requires different kinds of reasoning that do not fit in a single context
• Different parts of the task need different information that cannot all fit in one context
• You want to reuse specialist agents across multiple workflows
• The system needs to be able to recover from one agent failing without restarting the whole workflow

It does not make sense when a single agent can handle the task. The overhead of orchestration is only worth it when the complexity of the task genuinely requires multiple specialized contexts.

The Failure Modes Are Different

Traditional software fails predictably: a function returns an error, an exception is thrown, a timeout occurs. Agent failures are messier. An agent can succeed at its local task while the overall workflow fails because the agent’s output was wrong in a way that only becomes apparent later.

This means orchestration needs richer failure handling than traditional code: not just “did this succeed or fail” but “did this produce output that is good enough to proceed with.”

What You’ll Learn

• What orchestration actually means in multi-agent systems
• Where orchestration is warranted versus where a single agent suffices
• Why agent failures are different from traditional software failures
• The minimum failure handling that orchestration requires

Try It Yourself

Think about a workflow you currently run that might benefit from orchestration. Break it into stages. For each stage, ask: does this stage require different context or different reasoning than the other stages? If yes, it might be a candidate for a separate agent. If all stages use the same context and reasoning, a single agent with structured prompting is probably better.

What’s Next

Orchestration and AI workflows naturally lead to the question of what to build versus what to buy. The next post is about making those product decisions — building products with AI as a component.

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Part of the Claude Learning Journey series · Next: Building Products with AI as a Component