Instead of a single llm call for post analysis job completion cleanup through the orchestrator, have another agentic pipeline that processes the scripts that were generated by the analysis agent.
it should:
- assess the verdict_results.json and run api calls to the backend to update the hypothesis table
- update the profile.md and insights.md with whatever important nuance was observed in the report.md from the job
- potentially auto add hypotheses into the queue based on some confidence/novelty metric
Instead of a single llm call for post analysis job completion cleanup through the orchestrator, have another agentic pipeline that processes the scripts that were generated by the analysis agent.
it should: