AI Agent Coordination Patterns

Researched: 2026-01-10 Source: Web Intelligence MCP (staged in stg:web:u_z1p5:research:row_dfcf)

Overview

Multi-agent AI systems enable complex problem-solving through coordinated teamwork between specialized AI agents. This knowledge base page synthesizes research on coordination patterns and orchestration strategies.

Key Resources

1. Multi-Agent AI Systems (Aya Data)

URL: https://www.ayadata.ai/multi-agent-ai-systems-when-one-agent-isnt-enough-and-when-it-is/
Content: 30,560 chars
Focus: Real-world applications including autonomous vehicles and smart grids
Key Insight: Coordinating fleets through vehicle-to-vehicle communication

2. AI Agent Orchestration Patterns (Microsoft Azure)

URL: https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/ai-agent-design-patterns
Content: 46,745 chars
Focus: Enterprise orchestration patterns extending cloud design patterns
Key Insight: Unique challenges of coordinating AI agents vs traditional services

3. Multi-Agent Coordination Playbook (Jeeva AI)

URL: https://www.jeeva.ai/blog/multi-agent-coordination-playbook-(mcp-ai-teamwork)-implementation-plan
Content: 117,841 chars
Focus: MCP & AI Teamwork implementation strategies
Key Insight: Practical playbook for multi-agent coordination

Total Research Content

195,146 characters of research material on AI agent coordination patterns.

Pipeline Verification

✅ Web → Staging → KB flow tested successfully - Search: web.search for "AI agent coordination patterns" - Fetch: web.batch_fetch for top 3 results
- Stage: staging.stage to stg:web:u_z1p5:research:row_dfcf (TTL: 24h) - Store: kb.create to permanent knowledge base