DeepResearch signals completion through explicit status indicators, structured output, and the absence of ongoing processes. When the system finishes gathering and analyzing data, it typically updates its internal state or returns a specific completion flag via its API or interface. For example, if you’re programmatically interacting with DeepResearch, you might receive a response object with a status field set to completed, or a callback/webhook might trigger to notify your application. This is the most direct way to confirm readiness—relying on built-in mechanisms provided by the tool itself.
Another indicator is the presence of finalized output files or data structures. For instance, if DeepResearch generates a report, summary, or dataset, the appearance of these artifacts in a designated storage location (like an S3 bucket, local directory, or database) often signifies completion. You could monitor for the creation of a results.json file or check for the existence of a final_report.md in a specific folder. Additionally, the output might include metadata like timestamps or a completed_at field, which can be used to verify the recency and finality of the results. Tools like filesystem watchers or database triggers can automate this monitoring.
Finally, observe system resource usage or logs. If DeepResearch runs as a background process, a drop in CPU/memory usage or log entries like "Research phase completed" or "Results finalized" can indicate completion. For example, a long-running script might write "Process exited with code 0" to stdout upon success. If you’re using cloud-based services, platform-specific metrics (e.g., AWS CloudWatch alarms) or job status dashboards might show when the task transitions from running to stopped. Combining these signals with timeouts (e.g., failing after 24 hours if no completion is detected) adds robustness to avoid indefinite waiting.