Prompt to pipeline in minutes
Describe a data workflow. The agent builds the connector, schema, pipeline, and dashboard.
Describe a data workflow. The agent builds the connector, schema, pipeline, and dashboard.
Describe what you need. The agent builds the pipeline, authors validation rules, or generates the query. Your team reviews and promotes.
"Pull Fed rates, build the schema, wire a connector, create the pipeline, deliver to Snowflake." The data engineering agent reads your prompt, scaffolds every stage, and returns a reviewable DAG. You review and promote. No tickets. No sprint planning.
"Pull Fed rates, build the schema, wire a connector, create the pipeline, deliver to Snowflake." The data engineering agent reads your prompt, scaffolds every stage, and returns a reviewable DAG. You review and promote. No tickets. No sprint planning.
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Describe a workflow in plain English. The agent scaffolds source connections, transforms, validation steps, schedules, and delivery targets. Review the DAG and promote to production.
Ask a question in plain English. The query agent translates it to SQL against Aquata data models, enforces column-level permissions, and returns a formatted result set.
State the validation requirement in plain English. The agent generates the rule, assigns severity, and wires it into the target pipeline.
Ask the agent to tag PII columns, trace lineage from source to report, or query the audit trail. Access policies and data catalog updates through the same conversational interface.
One conversational interface that delegates to specialized sub-agents. The copilot picks the right tool for the job. Your team can also build custom agents on the same MCP catalog.
Analytics agents for ad-hoc queries and exposure analysis. Governance agents for PII tagging and catalog enrichment. This page gets a major upgrade as each category ships.
Describe what you need in plain English — the agent handles the rest.