Business rules, technical requirements, and organizational knowledge as instructions for agents.
MCP-based tools for adding any type of context to data workflows. Author policies, set conditions, enrich tables, validate uses, and expose that context for agents.
Business logic, data contracts, policies, lineage, access rules, and asset metadata as structured, machine-readable context.
Agents evaluate dynamic business rules instead of relying on hardcoded branches or human interpretation.
Context layers are versioned. A policy that changes doesn't break every system relying on it.
A decision-context layer for data agents. Surface the data and context behind a question, capture the judgment the agent should apply, audit and evaluate the decisions made.
Surface meaning
Context attached to every asset, field, and workflow.
Explore →Author policies
Rules authored in plain language, compiled to instructions agents read at runtime.
Explore →Trace decisions
Every allow, deny, or modify traces back to the rule that produced it.
Explore →Intercept execution
Agent decisions checked against policy. Patterns of incorrect behavior surface across runs.
Explore →Agents act on structure, not meaning
A column named revenue means different things in different tables. An agent sees a number. Without context, it can't tell the difference.
Rules live in the wrong places
Business logic is scattered across threads, wikis, and documents. None of it is readable.
Context disappears at every boundary
The moment data moves across a join, a pipeline stage, or an API call, the rules that governed it stay behind.
No record of why
When an agent produces a wrong result, there is nothing to trace back to. No rule it followed, no decision it logged.
Agents and systems make the same decisions across data workflows. No schema or semantic drift.
Context stored in separate systems
Passive documentation
Static schema and policies
Rigid implementations that break with updates
Structured, machine-readable context attached to data
Operational, decision-grade instructions
Adaptive decisions at runtime
Deploy once, version and evolve over time
Native warehouse integrations or cross-platform agent coordination. Adapts to how you work.
Deep native integrations means governance built into data development.
Multiple agents query the shared decision layer before accessing data. Structured context becomes the coordination protocol between agents.
Rules travel with the data they govern. Agents operate within constraints they can read.
Agents combine fields from different tables with the relationships and constraints already attached. No human translation required at each step.
Rules authored once apply across every agent, pipeline, and system that touches the data.
An agent that understands what it can't do doesn't need a human to stop it.