Is it possible to get intermediate updates or see what DeepResearch is doing during its research process?
No, DeepResearch does not provide real-time visibility into its internal steps or intermediate updates during execution. The system operates as a closed process, generating outputs after completing its analysis without exposing incremental progress. This is common in many AI-driven tools, which prioritize efficiency and focus on delivering finalized results rather than step-by-step transparency.
Why intermediate updates aren’t typically available DeepResearch, like many AI models, relies on complex algorithms and data processing steps that are abstracted from end users. For example, when analyzing a dataset or solving a problem, the system might perform tasks like data retrieval, filtering, statistical analysis, or pattern recognition—but these steps are not exposed in real time. This abstraction simplifies the user experience but limits insight into the workflow. Additionally, sharing intermediate states could introduce performance overhead (e.g., latency from frequent updates) or expose proprietary methods.
Workarounds or alternatives If you need visibility into a research process, consider breaking the task into smaller, manually orchestrated steps. For instance, instead of relying on DeepResearch to handle an end-to-end analysis, use its API to execute discrete operations (e.g., querying specific data subsets, running calculations) and log results at each stage. Alternatively, tools like Jupyter Notebooks or custom scripts can help replicate parts of the workflow with greater transparency. Some platforms also offer debugging modes or logging features for developers, though this depends on the specific implementation of DeepResearch.
Practical example Suppose you’re using DeepResearch to analyze customer feedback. Instead of requesting a full report, you could first run a sentiment analysis query, then a keyword extraction, and finally a trend summary—capturing outputs at each step. While this approach requires more effort, it provides incremental results and lets you validate progress. Without built-in intermediate updates, such manual segmentation is often the most reliable way to monitor a process.