The agent builds your pipelines, then runs them at your chosen autonomy
Data work is more than ETL — it's finding sources, handling format mismatches, validating quality, and making judgement calls. Describe the pipeline and Panaptic's agent builds it: connections to your sources, typed TypeScript code libraries for the transforms, and apps to inspect results. It runs replay-safe on every integration boundary, so a retry never double-writes, and you keep humans on the judgement calls.
What this looks like in practice
Each use case is a real shape the platform composes — not a feature list. The agent reads the same operations your team does, with confirmation gates on the steps that matter.
Pipelines the agent authors and types
Describe the flow and the agent builds connections to each source plus typed code libraries for the SQL and TypeScript transforms — verified against real API responses before the contracts are committed, not vibe-coded.
Reports generated end to end
The agent pulls from every connected source, analyses with the model your org chose, and builds an app to render formatted reports. Run it on a schedule fully autonomously, or in CoPilot mode to approve the figures before they ship.
Quality monitoring with human-reviewed fixes
The agent builds checks against your data tables and flags anomalies. Remediation runs at the autonomy you set — auto-correct the routine, route the ambiguous to a human at a confirmation gate, audit every change afterwards.
A queryable store, not a prompt window
State lives in the Task Store, a typed, path-addressable, version-tracked store the agent can query by path — so analysis works against structured, addressable data instead of whatever fits in the context window.
Ready to build data pipelines on Panaptic?
Spin up a workspace and run your first real task in minutes — or talk to us if you want a guided rollout for your data team.