How a data science agent fits your workflow

A data science agent connects reasoning with tools that inspect data, execute code and retain experiment results. OctOpus provides that workflow through a shared workspace and interfaces for programmatic use. Tool access and persistent evidence matter more than a conversational label.

Inputs and state

An agent needs a defined dataset, business objective and evaluation protocol. The workspace retains the plan, experiment history, scripts and resulting artifacts so the next action can be informed by what actually happened. Permission to connect a source is separate from permission to export or deploy its outputs.

Tools and integration

Teams can work in the browser or use the documented CLI, Python SDK and API. The MCP interface lets compatible assistants invoke OctOpus workflows intentionally. Integrations should preserve authentication, account ownership, resource limits and cancellation rather than hiding a long-running job behind a chat reply.

What a caller should verify

Treat completion, model quality and deployment readiness as separate states. Inspect errors and saved metrics, confirm the validation split, and retrieve the relevant code and artifacts before using a prediction service. A report generated by the agent is not independent evidence unless its claims can be checked against the run.

Key capabilities

• Persistent runs and inspectable artifacts.
• Authenticated APIs and workspace ownership.
• Explicit progress, cancellation and bounded execution.