sciomc

You can automate multi-stage codebase analysis by orchestrating parallel scientist agents that decompose complex research goals into independent stages, cross-validate findings, and aggregate evidence-backed results into consolidated reports with session state persistence.

Parallel multi-agent research orchestration works by firing multiple scientist agents simultaneously with model routing for data gathering, standard analysis, and complex reasoning, applying concurrency controls and verification loops to investigate hypotheses and synthesize findings.

Yes, you can run fully autonomous AUTO mode investigations with defined iteration limits, state persistence, and resume or cancel commands to manage long-running multi-stage research sessions without continuous manual intervention.

The best way to decompose a complex research goal is to break it into 3 to 7 independent stages with clear scope, tiering, and hypotheses, then fire parallel scientist agents to investigate each stage before cross-validating and synthesizing the findings.

You cross-validate research findings by running verification loops that identify conflicts, enforce evidence and confidence tags on agent outputs, and aggregate the verified data into a synthesized report with figures and session state.

Limitations of autonomous multi-agent orchestration include managing concurrency controls and iteration limits for long-running sessions, as complex codebase investigations require state persistence and resume commands to handle extended research runs without losing progress.