mm-orchestrator

You can automate a multi-stage research pipeline by executing 14 stages from configuration resolution to reflect in a single autonomous run, ensuring consistent, resumable, and auditable executions across complex workflows.

Multi-agent pipeline orchestration with parallel data collection dispatches concurrent tasks across 14 stages, integrating memory, debates, and synthesis to produce auditable research outputs with non-fatal error handling.

To resume an interrupted research pipeline run, the orchestrator uses date-aware workspace management, MCP-based status updates, and stage timing hooks to recover and continue stages without losing prior progress.

Yes, you can insert a human review gate at stage 13 of the pipeline, enabling controlled manual input before final release gating and reflect stages are executed.

The best way to handle errors in a multi-agent orchestration pipeline is using non-fatal error handling during immediate stage progression, allowing the 14-stage workflow to continue despite partial failures while maintaining auditable status updates.

Yes, you need a prepared workspace and a target date to invoke the orchestrator, which then executes all 14 stages from configuration resolution through reflect in a single run.