Phaidra's Raahul Singh and Vanč Levstik explain why raw context, vector retrieval and parallel sharding fail when an AI agent must resolve hundreds of thousands of similar equipment names. Context windows saturate, near-identical identifiers confuse semantic search, and repeated names can trigger model output limits.
Their replacement gives the language model a compact description of the facility hierarchy and asks it to produce a structured search plan. Deterministic code then traverses pre-indexed subtrees, applies filters and performs exact set operations. Cost therefore scales with tree depth instead of the number of sensors.
In tests, the old approach fell from 80 percent correctness at 64 GPUs to about 30 percent at 460,000. The hybrid system maintained 100 percent accuracy across the scale tests and 66 production cases while reducing one validation workload from 116 million tokens to 390,000.
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