# Multi-agent Coordination

> Delegation, specialization, shared state, handoffs, arbitration, budgets, and avoiding coordination overhead.

Canonical: https://learn.significanthobbies.com/curriculum/concepts/multi-agent-coordination

Delegation, specialization, shared state, handoffs, arbitration, budgets, and avoiding coordination overhead.

- Difficulty: core
- Tracks: Agent Systems

## Mental model

Multiple agents help when work decomposes cleanly and results can be verified independently. Shared goals, bounded tasks, explicit handoffs, and conflict resolution matter more than agent count.



## Primary sources

- [AutoGen: Enabling Next-Gen LLM Applications](https://arxiv.org/abs/2308.08155) (paper)

## Practice

### Design exercise: Multi-agent Coordination

Delegation, specialization, shared state, handoffs, arbitration, budgets, and avoiding coordination overhead. Implement designOutline() returning non-empty values for: taskDecomposition, sharedState, conflictResolution. Each value must name a concrete mechanism or decision.

**Expected evidence:** A design outline with taskDecomposition, sharedState, conflictResolution plus an explicit failure mode or trade-off.

## Review prompts

- When does splitting work across agents make results worse rather than better?

## Build evidence

- **Synthesize: Agent Systems** — Engineer useful agents with bounded loops, tools, memory, protocols, durability, permissions, and long-running control. Produce one working system, benchmark, or evidence-backed design that integrates the path.

## Prerequisites

- [MCP & Integrations](https://learn.significanthobbies.com/curriculum/concepts/mcp-integrations)

## Related concepts

- [MCP & Integrations](https://learn.significanthobbies.com/curriculum/concepts/mcp-integrations)
- [Agent Communication & Interfaces](https://learn.significanthobbies.com/curriculum/concepts/agent-communication-interfaces)

## Learning paths

- [12-Week Agent Systems](https://learn.significanthobbies.com/curriculum/roadmaps/agent-systems-12w)