Demos shine, production breaks: the hidden gap killing AI agents. Aditya Godbole reveals why vector memory isn't enough — and how knowledge graphs as reasoning substrates fix it for real-world chaos.

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AI agent demos work well in controlled environments but fail in production due to unbounded telemetry data overwhelming context windows. Aditya Godbole, speaking at Fifth Elephant Pune 2026, argues that vector memory alone is insufficient and proposes schema-driven knowledge graphs as a reasoning substrate for production AI agents. Key techniques include treating metrics as structured graph nodes/edges, using Jaccard similarity for fuzzy entity mapping, and enabling causal reasoning that handles real-world observability chaos at scale.

Making AI Agents work with Knowledge Graphs