The Fragility of Intent-Based AI Routing at Scale

Intent-based AI routing relies on a frontline LLM to dynamically send user requests to specialized tools or agents based on semantic descriptions. While this works in a sandbox, it completely shatters under real-world enterprise scale. As tool counts grow, semantic descriptions overlap, causing the router to misclassify inputs, trigger costly self-correction loops, and spike system latency. To fix this anti-pattern, engineers must build hybrid routing graphs: dividing capabilities into isolated namespaces, using deterministic code-based pre-filters for exact matches, and establishing strict confidence-score firewalls to catch and reroute low-certainty classifications immediately.Read All

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