star-plan-executor

You can automate research plan execution by orchestrating task subagents to modify code and run validations based on a structured plan. This approach bridges high-level research planning and concrete implementation without manual overhead.

Automated code-agent orchestration systematically executes leaf-level research sub-plans, verifies results, and structures documentation. It ensures reproducible implementation and experiment tracking while applying safety guardrails to prevent irreversible autonomous actions.

Ensure reproducible implementation by using automated execution that orchestrates subagents for code modifications and light validation. This provides structured documentation and idempotent state management for systematic AI research workflows.

Yes, you can use automated agents to scaffold code, run smoke tests, and log results for new model architectures. The workflow pauses for user approval before launching full-scale training to maintain safety.

Prevent heavy or irreversible experiments by implementing a strict STOP line guardrail. This enforces user-approved side effects, pausing automated execution before launching full-scale training or other high-impact operations.

No, you do not need to manually track state. The automated execution applies idempotent state management, ensuring environment-aware execution and structured documentation throughout the research workflow.