Agentic Data Science — Quickstart
Get your first autonomous data-science project running in 5 minutes. The Agentic Data Scientist (ADS) plans, executes, self-heals, and produces a cryptographically-sealed evidence bundle without manual orchestration.
Prerequisites
- A RadMah AI account (sign up at app.radmah.ai/signup)
- 25 credits — free tier comes with this out of the box
- A CSV or Parquet dataset under 100 MB
Upload a dataset
Dashboard → Datasets → Upload. Give it a clear name (e.g. customer_transactions_q1.csv) — the planner uses this name verbatim when selecting your data.
Create an agent project
Click Data Scientist in the top nav → New project. Fill in a title and a concrete goal:
Using customer_transactions_q1.csv, generate a forensic report that flags columns whose first-digit distribution deviates from Benford's law. Also produce a visualisation of the top 10 numeric column distributions.Review the plan
Within 3-15 seconds a plan appears — numbered steps with tool names, plus a total credit cost estimate at the top. Click Approve plan to begin execution.
Watch execution
Each step card updates live. Pulsing dot while running, rich result panels on completion (Benford histograms, forensic verdicts, standard tabular benchmark metrics). When a step fails quality checks, self-healing execution proposes a patch plan automatically — you'll see a self-healing badge.
Download the outputs
At completestate: step result panels show inline charts & tables, the Artifacts section lists every file, and the Crypto Trail panel has a Download replay bundle button that exports the full BLAKE3-sealed audit record.
What makes this different
Sealed evidence
Every project emits a cryptographically verifiable signed evidence bundle. Re-verify months later without trusting our infrastructure.
Typed memory
The planner remembers which tool sequences worked on which domain. Every new project in the same domain benefits from prior runs.
Self-healing
Failed quality gates trigger automatic patch plans, not opaque retries.
Bounded spend
Credit estimate shown before approval. Over-budget projects rejected up-front, never mid-execution.