Lessons learned from configuring Genie Agents

[mbellamybb]

Having started working with Genie Agents in Databricks, the dependencies that I was aware of for AI agents have been reinforced. 

Challenges encountered:

Business language can not be ambiguous

Common terms can have different meanings across departments within an organization. Addressing those differences in the semantic layer is essential to prevent agents from attempting to interpret user intent.

Metadata has a direct impact on response quality

In order to improve an agent's ability to select the appropriate data and generate accurate queries, there must be clear table descriptions, column definitions, synonyms and metric names.

Vague instructions lead to vague responses

Instructions which are too broad or written in an overlapping fashion can introduce uncertain responses from the agent. In order to produce better performance by the agent, there should be specific business rules and guidelines for the agent.

Example SQL is an essential part of configuration

Test queries add much needed context for agents. Tested queries assist the agent in understanding how common business questions should be answered. This is especially true where calculations involve particular filters, reporting periods or definitions.

Date context can be tricky and requires careful validation

Questions using terms such as “current”, “monthly” or “as of” can produce unexpected results, particularly if the underlying data uses a different date grain.

Alternative wording builds reliability 

While an agent can produce valid responses to a carefully written question, business users are likely to ask the same questions in a variety of ways. The agent’s ability to answer successfully regardless of the wording of questions requesting the same information establishes reliability.

Takeaways

My experience with configuring a Genie Agent has left me with the understanding that the process is as much a semantic modeling and governance process as it is an exercise in AI implementation.

My main takeaway is that agents become useful to organizations who have built a foundation of business rules, terminology and trust calculations.

[juanlozadab]

I agree with this. One thing I’d add is that this is where Domains and Pages can help a lot.

If every Genie Agent has its own definitions, instructions can quickly become messy. Terms like “active customer”, “current month” or “revenue” should not be redefined differently in each agent.

I see Pages as a good place to document those business concepts and metrics, and Domains as the boundary where that context belongs.

So instead of putting everything inside long Genie instructions, the context can live in a more governed place:

Domain → Page/business concept → trusted data assets → example SQL → Genie Agent

To me, that makes the agent easier to maintain and also easier for business users to trust. It becomes less about prompt tuning and more about building a proper semantic layer.