Fabric Data Agent: Fix Wrong Answers

Key insights

• Problem: A business user asks M365 Copilot via a Fabric Data Agent and gets a wrong number even though the connected Power BI report shows the correct figure.
  
• Root cause: The Data Agent relies on its instructions, example queries, and retrieval logic—so a correct semantic model alone does not guarantee correct answers.
  
• Fix pattern: First tighten the semantic model, then add clear agent instructions, refine example queries, and run evaluations to confirm changes improve results.
  
• Iterative tuning: Microsoft recommends an iterative workflow that uses repeated updates and evaluation runs to regression-test whether the agent now returns expected answers.
  
• Design guidance: Put core business logic, explicit measures, descriptions, and synonyms into the semantic model’s AI prep; use fewer curated sources and clear routing rules in the Data Agent instructions.
  
• Known issues & checks: Agents can behave inconsistently, ignore instructions, or return answers despite DAX errors—verify environment alignment, republish or recreate the agent, and re-run tests after each change.
  

In a clear and practical new YouTube demonstration, Reza Rad (RADACAD) [MVP] shows why a connected Power BI model can still produce wrong answers when queried through conversational tools. In particular, the video examines how a Fabric Data Agent connected to the correct semantic model returned an incorrect figure in M365 Copilot, even though the underlying report displayed the right number. Consequently, Rad walks viewers through the diagnosis and the precise fix he used, illustrating a broader lesson about how AI layers interpret business data. Ultimately, the video emphasizes that connectivity alone does not guarantee accurate conversational answers.

Problem Demonstrated in the Video

Rad begins with a concrete example: a business user asks a natural-language question in M365 Copilot and receives an incorrect response despite the report showing the correct metric. He highlights that the same semantic model properly powers interactive reports, which suggests the source data and measures are sound. However, the conversational path introduces an interpretation layer that changes how queries are routed and answered. Therefore, the mismatch points to the agent’s instructions and retrieval behavior rather than to raw data quality.

Next, Rad reproduces the issue inside the Fabric Data Agent console to inspect what the agent receives and how it builds its answer. He shows that the agent may prioritize different measures, apply different filters, or ignore key semantic definitions when example queries or instructions are not aligned. As a result, even identical business questions can yield divergent outputs across report visuals and the AI agent. Thus, the video frames the problem as an interaction between retrieval, instruction, and model metadata.

What’s Going On Behind the Scenes

Rad then explains the architecture: the agent reads the semantic model, but it also depends heavily on top-level instructions, example queries, and retrieval logic to interpret intent. In other words, the agent does not simply mirror the report; it assembles an answer based on which sources and examples it selects. Consequently, ambiguities in instructions or overly broad retrieval can lead to incorrect routing or the selection of unintended measures. Furthermore, he notes community findings that recommend clearer routing rules and curated model metadata to reduce confusion.

He also addresses known reliability issues reported by practitioners, such as differences between SDK and UI behavior, instruction mismatches, and cases where the agent returned answers despite DAX errors. These cases underline that the agent can sometimes ignore its own guidance, making troubleshooting harder. Therefore, Rad highlights the importance of an iterative approach that tests each change and confirms results in the conversational surface as well as inside the agent console. This stepwise testing helps isolate whether changes actually improve accuracy or introduce regressions.

Practical Fix: Using Agent Instructions

Rad’s solution focuses on refining Data Agent Instructions and improving example queries. He tightens routing guidance to specify which data sources and measures the agent should use for certain question types, and he clarifies expected response tone and detail. Then, he demonstrates testing directly inside the agent to validate the corrected instructions before publishing. As a result, the same business user question in M365 Copilot returns the expected, correct answer after the update.

Importantly, he follows Microsoft’s recommended iterative tuning workflow: start with the semantic model, refine instructions, update example queries, and run evaluations to measure improvement. Rad shows how evaluation runs compare actual responses against expected answers, allowing regression testing after each change. Consequently, this structured loop reduces guesswork and gives teams a repeatable way to improve conversational accuracy. Moreover, he stresses republishing and environment checks when behavior remains inconsistent.

Tradeoffs and Ongoing Challenges

While agent instructions help, Rad explains the tradeoffs involved in deciding where to put business logic. On the one hand, embedding logic in the semantic model centralizes rules and improves consistency for reports, but it can limit agility for agent-specific phrasing. On the other hand, moving too much into top-level agent instructions can create fragile, environment-dependent behavior that is harder to govern. Therefore, teams must balance stability with flexibility when designing metadata and instruction layers.

Additionally, the community still reports partial issues: agents ignoring instructions, inconsistent behavior between SDK and UI, and answers produced despite errors. These challenges mean that even with best practices, troubleshooting may require republishing, recreating agents, or engaging vendor support. Consequently, realistic expectations and systematic testing become critical parts of deployment planning.

Recommendations and Next Steps

Rad recommends a practical pattern: first tighten the semantic model, then add specific Data Agent Instructions, improve example queries, and run evaluations after each change. Furthermore, he advises testing fixes inside the agent console before publishing to the conversational surface, and then rechecking the business user experience. For teams, this means building an iterative workflow and documenting which guidance lives in the model versus the agent to avoid duplication and drift.

In conclusion, Rad’s video provides a useful, hands-on guide showing that conversational accuracy depends on careful instruction and iterative validation. By following the demonstrated steps and weighing the tradeoffs, organizations can reduce wrong answers and make conversational BI more reliable for business users. Ultimately, the lesson is clear: connect your models, but tune your agents.

Keywords

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