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An author describes how they solved scaling issues in a 200+ tool AI system by implementing predefined playbooks for tool sequences, improving reliability by avoiding model-improvised routes in multi-step executions.
The author shares lessons from building an agentic system at work, describing failures with a giant prompt, excessive tools, and dynamic sub-agents, and ultimately finding success with a fixed orchestrator and specialized child agents for each domain.