7.7 Limitations of Data Science Agent

While Data Science Agent offers numerous benefits, there are certain limitations that may impact its use in specific scenarios.

The current limitations of Data Science Agent include:

• Ad hoc SQL queries cannot be run directly
  
• Algorithms supported by Oracle permitted for models
  
• Conversation length and scope
  
• Error handling
  
• Performance and latency related limitations
  
• Reuse of existing objects
  

7.7.1 Ad hoc SQL queries cannot be run directly

Data Science Agent is capable of generating SQL internally to create views. However, it does not support running of ad hoc SQL queries or direct visualization of raw result sets currently.

Statistical analysis of Data Science Agent is highly effective when working with row-level datasets, and not aggregated outputs. However, some analyses on grouped data can be performed if the row count per group is large. Therefore, for more reliable analysis and modeling, use views that have ungrouped or only minimally aggregated data.

Note:

You can define arbitrary views to structure and transform data for downstream analysis and modeling.

7.7.2 Algorithms supported by Oracle permitted for models

Data Science Agent permits algorithms that are only supported by Oracle. Currently, the agent supports the following machine learning functions—Classification, Regression, Clustering, and Anomaly Detection.

Note:

Inference or scoring is not supported for Clustering and Anomaly Detection.

7.7.3 Conversation length and scope

While Data Science Agent can handle extended interactions, very long conversations may gather context that negatively affects clarity or performance. For extended work, consider starting a new conversation after a substantial number of interactions (around 50 messages), particularly when your objectives change.

7.7.4 Error handling

Data Science Agent may occasionally encounter constraints. For example, unsupported column types for modeling. These are typically resolved by adjusting the data or approach. If you encounter such constraint, ask the agent for suggestions on how to solve minor issues.

Note:

Oracle recommends refining prompts, adjusting goals, or re-running steps.

7.7.5 Performance and latency related limitations

Certain operations such as data discovery, feature analysis, and model training may require a few minutes to process. Model training on very large datasets can take even longer. During these operations, the conversation may not progress until the operation is completed. If you encounter such performance or latency related issues, you can start other conversations.

7.7.6 Reuse of existing objects

Data Science Agent may reuse existing objects—views or models, although starting from scratch is also an option. If you prefer that the agent doesn't reuse previous objects, you can state so in your response. Otherwise, the agent may refer to or reuse relevant objects created earlier—including those from other conversations—when they are manually associated or automatically discovered. This is done to save time by avoiding repeated creation of the same objects.

Note:

If several similar objects are available, make sure that you specify whether to reuse or recreate the objects.