10 Battle Scars from Building Agentic AI Analytics
Building production-grade agentic AI analytics requires far more than connecting an LLM to SQL; teams encounter pitfalls including framework over-reliance, non-deterministic query generation, unresolved business-term ambiguity, multi-step consistency failures, trust-eroding black-box answers, cold-start onboarding gaps, latency and cost blowouts, and observability gaps that make debugging impossible.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User submits NL query
A user submits a natural-language request to the analytics agent to answer a business question over company data.
Tools used
LangChain
Outcome
Tellius has implemented deterministic planning, a governed semantic layer, clarification policies, inline transparency, and observability in production; governed feedback and drift checks let the team correct issues quickly without asking users to prompt harder.
What failed first
Using generic LLM chain frameworks as the runtime hides retries, timeouts, and mutable state, making behaviour hard to trace. Letting generative prompts drive execution produces non-deterministic queries. Black-box feedback mechanisms erode user trust because changes persist with no visibility or review.