orchestrate

Multi-agent orchestration resumes from the last checkpoint by loading cached progress summaries and persistent stage checkpoints, ensuring previously completed work is not repeated. This behavior is built directly into the execution loop.

Deterministic stage checkpointing in a task execution workflow is the process of persisting progress summaries and execution state at fixed intervals. It enables reliable resume-from-last-checkpoint behavior and structured status reporting across coordinated multi-agent runs.

Parallel bulk execution and multi-model synthesis can both be run using a single orchestration pattern. You select an execution methodology, load an approved plan, and delegate the task to the corresponding sub-skills automatically.

An approved plan is required before starting autonomous idea-to-code iteration. The orchestrator loads the most relevant prior plan from a direct argument, ideation output, or cache, and requires a confirmation step before execution begins.

Trace-to-interview deep investigation should be used when a task requires exhaustive, multi-step inquiry rather than standard execution. It operates as a distinct orchestration pattern within the same checkpointing and progress tracking framework.

Missing rules, skills, and agents can be created on-the-fly during coordinated multi-agent runs. The orchestrator generates these resources dynamically when needed without unnecessarily blocking the execution workflow.