Why AIP Analyst Exists
If AIP Assist is a personal assistant that reacts to whatever you ask it in the moment, AIP Analyst is closer to an autonomous agent that carries an entire analysis task through from start to finish. It goes beyond simply writing SQL for you — it plans the analysis itself, executes code, and verifies the results.
How It Works
Think-Act-Observe loop
- Instead of producing an answer immediately, the LLM repeats a cycle of “what do I need to check (think) → run a query or code (act) → review the result (observe)” as it works through the analysis.
A multi-agent structure with divided roles
- Separate roles collaborate: one breaks a vague user request into concrete sub-tasks, another generates the actual queries or code, another executes that code in an isolated environment, and another verifies whether the results match business rules. Splitting the work this way lets errors get cross-checked, rather than relying on a single agent to catch its own mistakes.
Data security considerations
- Rather than sending raw data itself out to the LLM, the common approach is to work mainly with schemas and summary statistics.
- Role-based access control and data masking need to be applied together, or sensitive data can end up exposed during the analysis process.
What to Check in Practice
- Don’t feed analysis results directly into decisions: even though the agent runs code autonomously, the more consequential a conclusion is for the business, the more it needs a human to check the underlying logic once more.
- Check what data is actually being sent to the LLM: the security risk differs a great deal depending on whether it’s raw data or summarized statistics — it helps to clearly document these term definitions in advance.
Common Mistakes
- Delegating every repetitive analysis task without question: delegation pays off for repetitive data-extraction work, but handing over analyses full of frequent edge cases can actually increase the verification burden instead of reducing it.
- Applying it to sensitive data areas first, without a security review: if the domain involves sensitive data, it’s safer to first try it on lower-sensitivity analysis tasks, thoroughly verify the data flow, and only then expand its scope.
- Skipping documentation of term definitions: this is already a concern at the ontology design stage, but it’s worth checking once more, at the point you attach Analyst, that your business terms are clearly defined.
Next Steps
Looking at the other capabilities in the AIP series — Logic, Assist, Chatbot Studio, AI FDE — together makes it much clearer how AIP as a whole fits together around the Ontology.