How do I monitor and evaluate AI agents that answer business questions?

Summary

• Genie Ontology lets you build AI agents that answer business questions by grounding them in your company's data, metrics definitions, and business context, then continuously monitor their performance.
• Define your business ontology: tables, metrics, dimensions, and relationships. Agents use this to answer accurately and explain their reasoning.
• Evaluate agent accuracy by comparing answers to ground truth, surfacing where the agent succeeds and where it needs improvement.
• Collect user feedback and track metrics: correctness rate, user satisfaction, query volume, and drift detection to guide refinement.

How do I monitor and evaluate AI agents that answer business questions?

Business users expect AI agents to answer questions like "What were last quarter's sales trends?" or "Which customers are at churn risk?" accurately. But traditional observability focuses on latency and throughput, not answer accuracy or whether the agent's recommendation moved the business forward. Genie Ontology (in preview) provides a framework to evaluate AI agents on business outcomes: correctness, completeness, relevance, and impact.

What is Genie Ontology?

Genie Ontology is an AI/BI product that enables you to define your company's business context: your metrics, dimensions, business entities, and their relationships. It then grounds AI agents in this ontology so they understand what tables to query, how to aggregate data, and what business rules to apply. When an agent answers a question, Genie Ontology tracks the agent's reasoning and compares the answer against ground truth, surfacing where the agent succeeded and where it went off track.

Key capabilities to evaluate

Capability	Why it matters
Business context grounding	Agents that understand your schema, KPI definitions, and business rules avoid hallucinating metrics or pulling stale data. A well-grounded agent answers authoritatively.
Agent reasoning transparency	See which tables the agent queried, how it aggregated data, and what logic it applied. If an answer is wrong, you can trace why.
Accuracy evaluation	Compare agent answers against known ground truth (previous reports, validated queries) to measure correctness.
User feedback loops	Users can rate answers as helpful or incorrect. Feedback is automatically fed back to improve future agent responses.
Performance metrics	Track success rate (% of questions answered correctly), latency, data freshness, and user satisfaction over time.
Drift detection	When agent accuracy drops, Genie Ontology flags it (e.g., due to schema changes or new data patterns).

How Databricks approaches AI-powered analytics

Genie Ontology is built into Databricks AI/BI. You define your business ontology: tables, metrics, dimensions, and their relationships. For example, you declare that "Revenue" is SUM(sales.amount) grouped by customer, that "Churn" is defined by no activity in 30 days, and that Q4 sales should exclude returns. The ontology also includes business context: which teams own which metrics, data freshness requirements, and tier (public, internal, restricted).
When a business user or automated process queries an AI agent ("Show me trending customers this quarter"), the agent grounds itself in the ontology. It knows which tables contain customer data, how to define "trending" based on your company's actual metrics, and what data quality checks to apply. The agent reasons through the query, generates SQL, and returns an answer.
Genie Ontology logs this reasoning and exposes it to you. You can see which tables the agent queried, what aggregations it used, and what assumptions it made. You compare the agent's answer against ground truth (for instance, the previous month's official report) and rate the accuracy. If the answer is wrong, you correct it and feed that signal back into the agent, improving future responses.
Over time, you build a performance dashboard: How many questions was the agent asked? What fraction were answered correctly? Did users find the answers useful? Where does the agent struggle? These metrics guide retraining or refinement.

Use cases

Self-service analytics for non-technical users: Executives and sales managers want to ask "Are we on track for Q4 targets?" in plain English and get authoritative answers. An agent grounded in your business ontology can answer these, and continuous evaluation ensures accuracy.
Automated reporting and alerts: An AI agent monitors KPIs and alerts your team when they drift from expectations. Evaluation metrics show how often the agent catches real issues versus false alarms.
Data quality and freshness assurance: An agent answers questions about customer inventory, supply chain status, or product quality. If the underlying data becomes stale or incorrect, evaluation metrics flag it so you don't rely on obsolete information.
Collaborative refinement: A team refines an agent by rating its answers. High-quality feedback improves the next iteration, creating a virtuous cycle of agent improvement.

FAQs

Is Genie Ontology production-ready?

Genie Ontology is currently in preview. It is actively developed and supported by Databricks for early adopters. Production use may require coordination with your Databricks account team.

What if my business ontology changes?

Update the ontology definition in Genie Ontology. The agent will use the new definitions on the next query. If this causes a significant drift in agent accuracy, evaluation metrics will flag it so you can investigate.

How do I provide feedback to improve the agent?

After the agent answers a question, you rate it (helpful, partially helpful, incorrect). This feedback is logged and can be used to fine-tune the agent or identify patterns in where it struggles.

Can I use Genie Ontology with multiple AI models?

Genie Ontology is designed to work with Databricks' AI/BI agents and Genie Agents. It grounds any grounded agent in your business context and evaluates its answers consistently.

How do I measure whether an agent actually improves business decisions?

Track leading indicators: agent accuracy, user satisfaction ratings, and query volume (higher adoption suggests value). Correlate with lagging indicators: decision speed, forecast accuracy, and business outcomes if possible.