#1 on dbt Labs' ADE Workbench · 94% AI acceptance rate · 10X engineering velocity
YAML
With a simple YAML file your agent is ready to run. Define the agent's role, its goal, and tools it should have access and deploy to run in seconds.
CAPABILITIES
Every agent runs in its own isolated environment with access to your warehouse, repos, and tools. Spawn one or a hundred — the architecture scales with you.
Role, goal, tools - all yours
Read and write across repos in a single session
Each run - its own environment
Spawn child agents mid-run, delegate sub-tasks automatically
API
Python SDK, GraphQL API, or CLI — trigger any agent from any system. Build it into your CI,
your orchestrator, or your own internal tooling. No glue code required.
Start with seven production-ready reference agents from Paradime. Fork them, customize the prompt and toolset, then publish your version to your team's catalog.
When a pipeline fails, the Commander spawns parallel sub-agents to read logs, profile the warehouse, and notify owners — then posts a structured incident report with a proposed fix to Slack.
Auto-retries with smart backoff, isolates the failing model, generates a targeted fix in an isolated sandbox, and re-runs only what's downstream. Closes the loop with a PR for review.
Hunts idle warehouses, unclustered tables, and runaway queries. Files a weekly PR with commits that compound — typically 20–40% off the Snowflake bill in the first quarter.
Generates column-level descriptions, model docs, and lineage notes grounded in real query patterns. Bi-directional sync keeps your yaml, Confluence pages, and data catalog aligned.
Audits your dbt™ project for missing tests, infers what each model actually needs from its lineage and column types, and proposes the tests in a PR — no busywork, no boilerplate.
Reviews every dbt™ pull request for SQL anti-patterns, breaking schema changes, missing tests, and lineage impact. Posts inline comments and approves only when the model is production-ready.
