Beyond Simple Q&A: Building an Agent Orchestrator for Enterprise Analytics

It offers a practical path for organizations moving beyond single‑domain chat toward AI-powered self-service analytics that users can rely on. Organizations evaluating where this fits within their broader data strategy will find useful framing in what AI analytics requires from governed data.

The conversation emphasizes that success hinged less on a single model and more on governance, ontology, and modular orchestration. The result is a single surface that unifies structured data, document context, and BI assets while preserving access controls.

Why deterministic metric views underpin trust

Early attempts to answer natural‑language questions directly on the gold layer produced inconsistent figures, acceptable for unstructured content but unacceptable for finance and risk. Structured data cannot tolerate hallucination, and 80 percent accuracy is a non‑starter for commitments or budgets.

Certified metric views solved the problem. By standardizing names, enforcing data contracts and quality thresholds, and attaching a shared business glossary in Unity Catalog, each domain‑specific Genie agent could answer deterministically. The same question yields the same answer every time, which is the baseline for enterprise adoption.

Supervisor-led orchestration across domains

Real user questions span domains. Asking about commitments in a country and the actions tied to those commitments involves multiple expert agents. Rather than pack everything into one space, the team built a LangGraph‑based supervisor that uses a ReAct pattern to decompose questions, route sub‑queries to the right Genie agent, and synthesize a final response.

The orchestration stack layers four capabilities on top of certified Genie agents: an intent classifier, a domain router, a query decomposer, and a response synthesizer. Additional agents handle unstructured document retrieval via vector search and discovery of existing BI reports. Each sub‑agent runs on behalf of the end user and inherits Unity Catalog permissions, so the supervisor never bypasses access controls. A maintained enterprise domain taxonomy, mapping domains, subdomains, shared entities, and owners, enables reliable LLM routing and a modular design where new agents plug in without rewriting logic.

Traceability and continuous evaluation keep agents reliable

Every step of the multi‑agent flow is observable. MLflow traces capture intent detection, routing choices, Genie‑generated SQL, latency, and token cost. That end‑to‑end visibility helps teams pinpoint whether a wrong answer stems from routing or from a specific agent.

Evaluation is continuous. The team curates a growing set of real questions with expected facts and routing targets, scored by LLM judges for correctness, relevance, and proper routing. A custom "no fabricated numbers" judge verifies that cited figures are grounded in Genie outputs. The same judges also run online against live traffic to catch drift and reinforce data discipline.

A pragmatic path to adoption

Enterprises do not flip a switch. Start with context enrichment and an ontology that expresses domains, relationships, and shared entities. Build and certify one Genie agent at a time against metric views, with verified Q&A and clear instructions. Add knowledge agents for document context and BI discovery once single‑domain agents are reliable. Only then scale to a supervisor that orchestrates across domains, with SMEs continuously reviewing traces, refining instructions, and expanding gold‑layer and metric‑view coverage.

Governance makes conversational analytics dependable at scale

The central lesson is that trustworthy enterprise AI analytics comes from deterministic metric views, a well‑maintained domain taxonomy, and a governed supervisor pattern that stitches expert agents together. With those foundations in place, organizations can turn fragmented data estates into a single conversational surface that users trust for financial, operational, and risk questions.