multi-agent-orchestration

Overview

Single agents are limited by context window, specialization depth, and parallelism. Multi-agent systems overcome these limits by routing subtasks to specialized agents. But multi-agent systems introduce new failure modes: lost context, conflicting decisions, infinite loops, and cascading failures.

This skill provides the architecture and coordination patterns to build multi-agent systems that are reliable, observable, and maintainable.

When to Use

• The task requires more context than a single agent can handle
• Different subtasks require different specializations (research, coding, review, security)
• Subtasks can be parallelized for speed
• The workflow is long-running and requires checkpointing
• Different tasks require different levels of human oversight

Process

Step 1: Design the Agent Network

1. Define agent responsibilities: Each agent should have a single, well-defined job. Name them by role: researcher, coder, reviewer, security-auditor, tester.
2. Define communication topology: Who can talk to whom?
   • Pipeline: Agent A → Agent B → Agent C (sequential)
   • Supervisor: Orchestrator dispatches to specialists (hub-and-spoke)
   • Peer: Agents collaborate as equals (mesh)
3. Define data contracts: What does each agent receive? What does it output? Use structured formats (JSON schemas) for inter-agent communication.
4. Define the orchestration logic: Who decides which agent acts next?

Verify: You can draw the agent network on a whiteboard with clear roles and data flow.

Step 2: Implement Context Management

1. Each agent should receive only the context it needs — not the full conversation history.
2. Use a shared state store (database, key-value store) for information that multiple agents need.
3. Pass summaries, not full transcripts, when context must traverse agent boundaries.
4. Include a task ID in every message for tracing.

Verify: No agent receives more context than it requires for its specific task.