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When teams first experiment with agentic AI, the journey often starts with a slick demo: a few APIs stitched together, a large language model answering questions, and just enough smoke and mirrors to impress stakeholders. When teams first experiment with agentic AI, the journey often starts with a slick demo: a few APIs stitched together, a large language model answering questions, and just enough smoke and mirrors to impress stakeholders.
But as soon as those demos face enterprise requirements — constant data ingestion, compliance, thousands of users, and 24×7 uptime — the illusion breaks. Services stall at the first failure, query reliability plummets, and regulatory guardrails are nowhere to be found. What worked in a five-minute demo becomes impossible to maintain in production. But as soon as those demos face enterprise requirements — constant data ingestion, compliance, thousands of users, and 24×7 uptime — the illusion breaks. Services stall at the first failure, query reliability plummets, and regulatory guardrails are nowhere to be found. What worked in a five-minute demo becomes impossible to maintain in production.
![Failure mode map — “From POC to production](/blog/case-study-trustgraph/failure-map-poc-to-production.png) ![Failure mode map — “From POC to production](/blog/case-study-trustgraph/failure-map-poc-to-production.png)
*Failure mode map — “From POC to production.”* *Failure mode map — “From POC to production.”*