How Structured Decomposition Makes AI Agent Workflows Production-Ready

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A software engineer at BizFlowAI has outlined a practical framework for building multi-agent AI workflows that remain stable under real-world, unpredictable inputs rather than clean demo conditions. The approach centers on mapping every workflow as a typed sequence of functions before introducing any LLM, clearly separating steps that require judgment from those that can be handled by deterministic code. The engineer's content pipeline assigns LLMs only to creative and analytical decisions, while schema validation, SEO checks, and API calls are handled entirely in code, which he says reduced errors to near zero. Tool selection is identified as a major failure point, with production data suggesting correct tool selection drops from 96–99% with 3–5 tools to below 70% when agents are given more than 20 tools. The recommended fix is a dispatcher-agent architecture where a small routing agent delegates to focused sub-agents, each limited to a narrow, well-described toolset.

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ProgrammingDEV Community ·

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