Multi-Agent Orchestration

Why This Matters

Complex tasks often exceed what a single LLM call can handle. Multi-agent orchestration breaks work across specialized agents that collaborate, each with their own tools, context, and expertise.

The Intuition

Think of a software team. You wouldn't ask one person to design, code, test, review, and deploy everything single-handedly. Instead, specialists handle their domain and hand off work. Multi-agent systems work the same way — an architect agent plans, a coder agent implements, a reviewer agent checks quality.

Patterns

1. Sequential Pipeline

Agent A (Plan) → Agent B (Execute) → Agent C (Review) → Done

Simple, predictable. Good for well-defined workflows.

2. Supervisor Pattern

         Supervisor Agent
        /       |        \
   Agent A   Agent B   Agent C

A coordinator decides which agent handles each subtask. Good when routing matters.

3. Debate / Consensus

Agent A ──→ critique ──→ Agent B ──→ critique ──→ Final

Agents challenge each other. Good for high-stakes decisions.

4. Swarm

Agents publish messages to shared channels. Any agent can pick up work. Good for parallel, independent tasks.

Design Considerations

Concern	Approach
State passing	Shared memory, message queue, or function args
Error handling	Retry, fallback agent, human escalation
Token budget	Each agent has its own context window
Coordination overhead	More agents = more LLM calls = more cost & latency
Observability	Log every agent decision for debugging

Common Failure Modes

Infinite loops: Agent A asks Agent B, who asks Agent A. Fix: max depth, cycle detection.
Context loss: Information gets lost between handoffs. Fix: structured state objects.
Role confusion: Agents duplicate work. Fix: clear tool/scope boundaries.