Enterprise AI Agents

A global enterprise needed domain-specific Q&A capability across several business functions, grounded strictly in its own curated, approved knowledge — not open-ended generation that could hallucinate policy or product detail. We stood up 6 production-grade Q&A agents with a practical evaluation framework, from kickoff to handoff, in 1 month.

Enterprise Q&A has a lower tolerance for error than consumer chat. An agent that answers confidently but incorrectly on internal policy, compliance, or product detail is worse than one that says "I don't know" — it erodes trust with the very users it's meant to help. The brief was to build 6 agents, each scoped to a distinct domain, that would only answer from a curated and approved knowledge base, decline gracefully outside that scope, and do it all inside a 30-day window from kickoff to handoff.

Grounding isn't a feature you bolt on at the end — it has to shape retrieval, prompting, and evaluation from day one, or the agent will confidently answer questions it was never supposed to touch.

We treated knowledge curation as the critical path, not a preprocessing afterthought:

• Source curation — every one of the 270 knowledge files feeding the 6 agents was reviewed and approved before ingestion, not scraped wholesale from internal drives.
• Retrieval architecture — domain-scoped indices per agent, so a query to one agent can't surface knowledge belonging to another domain.
• Refusal design — explicit guardrails so an agent declines rather than guesses when a query falls outside its grounded knowledge.
• Iterative tuning — retrieval and prompting refined against real failure cases surfaced during evaluation, not just a single tuning pass.

A practical evaluation framework has to answer one question for every agent, every domain: does this response actually reflect the approved knowledge, or is it drifting? We built that framework around a structured query set rather than ad hoc spot-checking:

The 720 test queries were distributed across all 6 domains to stress both correctness — does the agent answer accurately from its grounded knowledge — and boundary discipline — does it decline cleanly on out-of-scope questions instead of improvising.

A 30-day build only pays off if the enterprise can run the agents without us in the room. Handoff included the evaluation harness itself, not just the agents — so the client's team could re-run the same 720-query suite against future knowledge-base updates and catch regressions before they reached end users. Each agent's domain boundary, refusal behavior, and retrieval configuration were documented as part of the deliverable, giving the internal team a clear owner and a clear change-management path for every one of the 6 agents going forward.

The deliverable isn't 6 agents that work on day one. It's 6 agents plus the evaluation harness that proves they still work on day two hundred, after the knowledge base has changed underneath them.

Six domain-specific Q&A agents, grounded in 270 curated and approved knowledge files, validated against 720 test queries, delivered from kickoff to handoff in 1 month — a production-ready deployment on a timeline that would typically only cover discovery for a project of this scope.