Scene Routing: Why Single Omni-Agents Fail and How Splitting Into 6 Specialists Fixes It

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A developer building a commercial AI agent system found that equipping one agent with 20-plus tools caused frequent wrong-tool selections, such as triggering a contact search when a user asked about shipping costs. To solve this, a scene-routing architecture was introduced, where a classifier layer first identifies the type of user request and then directs it to one of six specialized worker agents, each with its own restricted tool whitelist and injected standard operating procedure. The routing classifier uses regex matching as a first pass, falling back to semantic matching for edge cases, ensuring nearly all requests are correctly categorized. After implementing scene isolation, the tool mis-selection rate dropped from 15 percent to under 2 percent, while context length was reduced by 60 percent and tool-call accuracy improved by 40 percent. The author frames this as an architectural mindset shift — favoring specialized, reliable agents over a generalist design — and plans to cover the full engineering framework across six articles.

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