Orchestrating 10 AI Agents: Patterns That Actually Work
The article discusses effective patterns for orchestrating multiple AI agents, moving beyond the fantasy of agent swarms and focusing on the engineering challenges.
Why it matters
These patterns can help developers build more robust and scalable multi-agent AI systems, moving beyond the initial hype and addressing real-world operational challenges.
Key Points
- 1Use a router pattern with an explicit dispatch table to route tasks to the appropriate agent
- 2Implement a supervisor pipeline with stage-specific responsibilities to coordinate the workflow
- 3Employ parallel spawn with serial fallback to handle provider rate limiting
- 4Use push-based status reporting instead of chatty polling
- 5Explicitly handle model switch failures, timeout cascades, and provider fallback
Details
The article emphasizes that orchestrating multiple AI agents is a systems problem, not just a prompting trick. The author shares the patterns they found effective, including a router pattern with an explicit dispatch table to route tasks, a supervisor pipeline to coordinate the workflow, parallel spawn with serial fallback to handle provider rate limiting, push-based status reporting, and explicit handling for various failure scenarios. The key is to have an explicit architecture instead of letting each component freestyle its role.
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