researcher

Automate research workflows by orchestrating planning, execution, measurement, and logging of experiments. The system transforms vague questions into structured experiments, applying guardrails and spawning subagents to ensure isolation and clear handoffs.

An autonomous experiment workflow manages end-to-end research cycles. Subagent coordination works by spawning scoped agents for isolated tasks, ensuring clear handoffs, enforcing discipline rules, and recording provenance and metrics for reproducibility.

Start an autonomous research session by describing your measurable objective to the system. It will automatically plan the experiment, execute scoped tasks via subagents, and log results with versioned artifact management.

You can enforce experiment discipline and data integrity across any domain with measurable outcomes. The system applies guardrails to the research cycle, manages versioned artifacts, and maintains a local experiment ledger directory for reproducibility.

Autonomous research logging tracks provenance and metrics by managing a local experiment ledger directory. It records all experiment cycles, scoped subagent handoffs, and data integrity rules to ensure full reproducibility of measurable outcomes.

Autonomous subagents require measurable outcomes to function effectively and rely on a local ledger directory for provenance. Without clear scoped tasks and enforced guardrails, isolation and clear handoffs may be difficult to maintain.