parallel-cli

Deep web research is automated using non-interactive jobs that return machine-readable JSON outputs with strict citation handling. This approach supports multi-step extraction, enrichment, and recurring monitoring for agent workflows.

Yes, you can enrich tabular inputs and discover datasets via entity discovery. This process returns structured results for reliable downstream synthesis, filtering, and reuse in code or planning contexts.

The best way to monitor recurring changes is by using async job control with no-wait execution and status polling. This pattern detects ongoing changes rather than performing single one-shot lookups, returning structured data with citations.

Yes, it is designed for long-running agent workflows. You launch a deep research task and then poll for status and results when the work takes longer than a single response cycle, ensuring reliable execution.

Citations are handled strictly based on returned CLI URLs within the JSON output. The structured search and extraction process ensures all machine-readable data includes source URLs for reliable downstream synthesis and verification.

You should avoid async web research jobs for simple one-shot lookups. This approach requires JSON-first, non-interactive execution patterns and is built for multi-step extraction, enrichment, and ongoing monitoring rather than quick single answers.