Multi-Agent Orchestration System

What you'll learn

• Orchestrator vs. peer-to-peer patterns
• Defining agent roles and tool boundaries
• Shared state and context passing between agents
• Handling agent failures gracefully

Architecture

User query → Orchestrator
  → decomposes into subtasks
  → Worker A (research), Worker B (analysis), Worker C (writing)
  → each returns partial results
  → Orchestrator merges → final output

Steps

1. Define 3 worker agents with distinct roles and tools
2. Build the orchestrator: receives query, decomposes, assigns, merges
3. Implement shared memory (conversation store + task state)
4. Test with a query that genuinely requires multiple perspectives
5. Add failure modes: what happens when one worker errors?
6. Measure: total tokens used, latency, output quality vs. single-agent baseline
7. Document when multi-agent wins vs. a single better prompt