Autonomous data science with human oversight

Autonomous data science delegates repeated investigation and experiment work to software while people retain responsibility for the objective and use of the result. In OctOpus, autonomy is bounded by the plan, available data, permissions and compute limits.

A loop, not an unattended promise

The workflow starts by profiling data and proposing a plan. After authorization, the agent writes and executes an experiment, examines its result, and chooses what to investigate next. A failed experiment can lead to a revised approach; it must not become a fabricated success. Iteration stops when limits are reached, the run is canceled or the workflow finishes.

Define the boundary before starting

Agree on the prediction target, horizon, split strategy and metric. Keep future information out of training features. Set the allowed experiment count and compute allowance. For example, a demand forecast needs chronological validation and a seasonal baseline, while a churn model may need customer-separated evaluation.

Review evidence before operational use

Inspect the scripts, comparisons, saved predictions and validation evidence. You can steer or cancel a run and continue from saved artifacts. Human approval remains necessary for business deployment and consequential decisions. Autonomous execution is not a claim of guaranteed accuracy, compliance or unrestricted access.

Key capabilities

• Set the objective and resource limits.
• Inspect failures as well as successful experiments.
• Separate model selection from final held-out evaluation.