Multi-Agent Coordination Methodology

Status: Production-Ready Success Rate: 100% (validated across multiple sessions) Last Updated: 2025-10-02

Overview

This guide documents the proven methodology for coordinating multiple AI agents to work in parallel on well-scoped, independent tasks. This approach has been successfully used to coordinate 10+ specialized personas with zero conflicts and 100% completion rates.

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Table of Contents

1. When to Use Multi-Agent Coordination
2. Core Principles
3. Planning Phase
4. Execution Phase
5. Integration Phase
6. Tools & Technologies
7. Case Studies
8. Troubleshooting

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When to Use

Ideal Scenarios

✅ Use multi-agent coordination when:

• You have 5+ independent tasks that can run in parallel
• Tasks have clear, well-defined scopes and success criteria
• No blocking dependencies between tasks
• Each task maps to a specific domain of expertise
• You need to maximize throughput and minimize time-to-completion

Not Recommended

❌ Don’t use multi-agent coordination when:

• Tasks have complex dependencies requiring sequential execution
• Scope is unclear or requires discovery/exploration
• Tasks involve the same files/code (high conflict risk)
• Single task can be completed faster than coordination overhead
• You need iterative feedback loops between tasks

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Core Principles

1. Independence

Each agent must be able to complete their task without waiting for other agents.

• No shared file edits
• No blocking dependencies
• Clear boundaries between work areas

2. Specialization

Match each task to an agent with the right domain expertise.

Task Type	Recommended Agent Type
Testing & QA	quality-engineer
Security & Auth	security-engineer
API Documentation	technical-writer
Infrastructure	devops-architect
Frontend/UX	frontend-architect
Performance	performance-engineer
Refactoring	refactoring-expert
System Design	system-architect
Code Quality	python-expert (strong typing)

3. Clear Success Criteria

Every agent must know exactly what “done” looks like.

Example success criteria:

## Success Criteria
- Zero test files remain in /src directory
- All imports updated correctly
- Jest discovers all migrated tests
- At least one test runs successfully

4. Conflict Prevention

Proactively design work areas to avoid conflicts.

Spatial Separation:

• Agent A: /src/lib directory
• Agent B: /src/app/api directory
• Result: Zero overlap, zero conflicts

File Type Separation:

• Agent A: Test files only
• Agent B: Source code only
• Result: Different file sets, zero conflicts

5. Coordination Layer

Use a coordination agent to orchestrate, monitor, and integrate.

The coordinator:

• Reviews all deliverables
• Checks for conflicts
• Updates tracking systems (GitHub, TODO.md)
• Creates integration reports
• Identifies blockers

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Planning Phase

Step 1: Decompose the Work

Break down the high-level goal into atomic, independent tasks:

Goal: Improve codebase quality

Task Breakdown:
1. Migrate test files (Quinn - quality-engineer)
2. Enable TypeScript strict checks (Morgan - python-expert)
3. Fix production build (Riley - devops-architect)
4. Document API endpoints (Jordan - technical-writer)
5. Migrate console.log (Casey - refactoring-expert)
6. Optimize Docker layers (Finn - devops-architect)

Step 2: Dependency Analysis

Create a dependency graph to verify independence:

graph LR
    A[Quinn: Tests] --> INDEPENDENT
    B[Morgan: TypeScript] --> INDEPENDENT
    C[Riley: Build] --> INDEPENDENT
    D[Jordan: API Docs] --> INDEPENDENT
    E[Casey: Logger] --> INDEPENDENT
    F[Finn: Docker] --> INDEPENDENT

If dependencies exist, either:

• Sequence dependent tasks
• Find alternative decomposition
• Reduce scope to eliminate dependency

Step 3: Conflict Analysis

File Overlap Matrix:

	Agent A	Agent B	Agent C
Agent A	-	Check	Check
Agent B	Check	-	Check
Agent C	Check	Check	-

For each pair, verify:

• Different directories? ✅
• Different file types? ✅
• Different concern layers? ✅

Step 4: Create Agent Specifications

For each agent, document:

## Agent: [Name] ([Domain])

**Task:** [One sentence description]
**Issue:** #[number]
**Agent Type:** [e.g., quality-engineer]

**Scope:**
- File patterns: src/lib/*.ts
- Exclusions: tests/, __mocks__/
- Actions: Read, Edit, Write

**Success Criteria:**
- [ ] Criterion 1
- [ ] Criterion 2
- [ ] Criterion 3

**Deliverables:**
1. [Specific artifact 1]
2. [Specific artifact 2]
3. GitHub comment to issue #[number]

**Constraints:**
- Don't modify [X]
- Stay within [directory]
- Follow [pattern]

Step 5: Use MCP Sequential Thinking

Before deployment, use sequential thinking to validate the plan:

// Pseudocode
sequentialThinking({
  thought: "Analyze dependency graph for persona coordination",
  verifyIndependence: true,
  checkConflicts: true,
  estimateTime: true
})

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Execution Phase

Parallel Deployment

Deploy all agents in a single message for maximum parallelization:

// Deploy 10 agents simultaneously
<function_calls>
  <invoke name="Task" subagent_type="quality-engineer">...</invoke>
  <invoke name="Task" subagent_type="python-expert">...</invoke>
  <invoke name="Task" subagent_type="devops-architect">...</invoke>
  // ... 7 more agents
  <invoke name="Task" subagent_type="system-architect">...</invoke>