Aiengineering

Diagram showing the operating gap between an AI POC and production AI

From AI POC to production: the part teams keep skipping

The AI POC is not the hard part anymore. The hard part is turning a promising demo into a service with ownership, evals, traces, cost controls, and a rollback path.

Diagram showing metric-only LLM observability versus a replayable production AI trace

LLM observability is not a dashboard. It is a replayable trail.

A latency chart will not explain why an AI answer was wrong. Production LLM systems need traces, sources, tool calls, prompt versions, eval results, and human decisions.

AI agents in financial services need controls before autonomy

Financial-services AI agents can be useful, but autonomy without permissions, audit trails, segregation, evals, and rollback is just operational risk with a nicer interface.