Multi-Agent Orchestration - From Scripts to Swarms

You give a single agent a complex task. It researches the topic, writes code based on the research, then writes tests for the code.

By the time it gets to the tests, it's forgotten half the research. It starts making up API signatures.

That's Context Window Saturation. And the only real fix is to stop pretending one agent can do everything.

Specialization

Think about how a good engineering team works. One person doesn't write requirements, implement the code, review it, write the tests, and deploy it.

You have specialists.

Same principle applies to agents. The moment you split a monolithic agent into a Researcher, a Coder, and a Reviewer — each with a focused context window and a clear mandate — everything gets dramatically better.

Not incrementally. Dramatically.

The Supervisor Pattern

One high-reasoning agent acts as the router. It reads the incoming request, decides which specialist to call, and manages the flow.

Why does this work?

Clean context. The Coder never sees the raw research documents. It gets a structured summary from the Supervisor. No wasted tokens on irrelevant information.

Focused accuracy. An agent that only writes code is measurably better at writing code than a generalist.

Parallelism. While the Coder works on feature A, the Researcher can be gathering information for feature B.

The Hard Part: Handoffs

Splitting into multiple agents is the easy decision.

How does Agent A pass the baton to Agent B without losing the thread?

The Shared Blackboard

All agents read from and write to a central state object.

interface AgentBlackboard {
  task: string;
  research: ResearchResult | null;
  code: CodeArtifact | null;
  review: ReviewResult | null;
  tests: TestResult | null;
  status: 'researching' | 'coding' | 'reviewing' | 'testing' | 'complete';
}

Simple. Intuitive. Works well for small systems.

The problem? When two agents write to the blackboard at the same time, you get race conditions. When an agent reads stale state, you get inconsistency. Same problem as shared mutable state in any concurrent system — same solutions too.

Explicit State Transitions

The alternative is a graph-based orchestrator where transitions only happen on specific conditions.

The key insight: the agent doesn't decide which state it's in. The orchestrator does.

The agent proposes actions. The orchestrator validates them. The state machine enforces the allowed transitions.

This is the difference between an agent that "usually works" and one you can actually deploy.

Failures Are Tuesday

Your agents will fail. The Reviewer will time out. The Coder will produce code that doesn't compile. The Researcher will return empty results.

This isn't an edge case.

Your orchestration layer must be idempotent. If the Reviewer crashes halfway through, the system should resume from the Coder's last output — not restart the entire pipeline.

async function executeWithCheckpoint<T>(
  stepId: string,
  fn: () => Promise<T>,
  store: CheckpointStore
): Promise<T> {
  const cached = await store.get(stepId);
  if (cached) return cached as T;

  const result = await fn();
  await store.set(stepId, result);
  return result;
}

Not glamorous. But it's the difference between a demo and a system that runs unsupervised overnight.

When Not to Use Multi-Agent

Multi-agent isn't always the answer.

If the task fits in a single context window and doesn't require different "modes" of thinking, a single well-prompted agent is simpler, faster, and cheaper.

Reach for multi-agent when:

• The task needs more than ~20k tokens of context across different knowledge domains
• Different steps need fundamentally different system prompts
• You want to parallelize independent subtasks
• You need audit trails showing which agent made which decision

For everything else? One agent is fine.

Don't over-architect.