How to Build and Govern Production-Grade Multi-Agent Systems
summary
Production-grade multi-agent systems require concrete architecture and governance: use sandboxing (Docker, zero host access), an agent-to-agent interoperability protocol, and separate short- and long-term memory via a Memory Bank to preserve session context. Adopt modular capability files like Skills.md, standardize coordination with frameworks such as OpenClaw, deploy on a runtime (Gemini Enterprise suggested), and implement planner–scorer co-training, preference-pair synthetic data, adversarial red-team testing, and a token-budget monitor to prevent runaway costs.
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chapters (14)
- 0:00Multi-agent systems intro
- 2:19LLM orchestration levels
- 5:12Security risks in frameworks
- 7:30Multi-agent frameworks
- 9:45Architectural patterns
- 10:36Hybrid planner-scorer
- 12:53Agent evaluation metrics
- 14:43Synthetic data generation
- 15:50Agent training process
- 17:20Adversarial testing
- 19:14Tokenomics and costs
- 20:43Measuring business value
- 23:16Fault tolerance patterns
- 25:32Q&A
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