MCP-based tools that add context to data workflows, for reliable agent decisions.
Business rules, technical requirements, and organizational knowledge as instructions for agents. Author policies, set conditions, enrich tables, validate uses, and expose that context for agents.
A decision-context layer for data agents. Surface the data and context behind a question, capture the judgment the agent should apply, and keep a log of the requests agents make.
Business logic, data contracts, policies, source provenance, 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.
Data agents fill gaps with guesses. Those guesses compound across every run.
A column named revenue means different things in different tables. An agent sees a number. Without context, it can't tell the difference.
revenue_amount
3 conflicting definitions
Business logic is scattered across threads, wikis, and documents. None of it is readable by an agent.
retention_policy
No authoritative source
The moment data moves across a join, the definition of a field can change. The agent has no way to know.
customer_id
Definition changes across join
When an agent produces a wrong result, there is nothing to trace back to. No rule it followed, no decision it logged.
output_methodology
No decision log found
The business rules, policies, and procedures an agent needs to act, authored and available to query.
The business logic and requirements are available for an agent to query as it works.
A rule authored once is available to every agent and workflow that asks, on any connected platform.
With the rules available to query, an agent resolves the situation instead of guessing.