Why does AI agent governance need its own playbook?
BI tools were governed at the front end - the user picked "Revenue" from a dropdown and the semantic layer resolved it. AI agents broke that. They generate text that may or may not match a real entity; they hallucinate joins; they invent column names; they confuse two metrics with similar definitions. Without a layer between the agent and the warehouse, every query is a roll of the dice. Governance for AI agents has to be structural, not advisory - a property of the compiler, not a wrapper around the answer.
Step 1: Pin every agent call to a real identity
The first failure mode is running the agent as a service account. Every tool call must carry the requesting user's identity, role, persona, and scope. Without identity, governance has nothing to enforce against. This means SSO/OIDC/SAML pass-through to the agent runtime - not a static API key.
Step 2: Resolve every term through a typed semantic graph
Replace string-matching against table and column names with resolution against a typed semantic graph. The same string ("revenue") must resolve to different concepts depending on identity and scope. This is the structural antidote to LLM hallucination - the agent cannot fabricate a concept that does not exist in the graph. See semantic graph in the glossary.
Step 3: Constrain planning to proven join paths only
Use a constrained planner that searches the graph for valid join paths and refuses to fabricate joins the graph cannot prove. Failed planning produces a structured error, not a guessed answer. This is the difference between a query and a guess.
Step 4: Inject RBAC, ABAC, and row/column predicates at compile time
Governance happens inside the compiler, before any SQL leaves the planner. Unauthorised queries fail compilation; the data is never read. Post-query filters do not count - by the time you filter, the data has already been accessed. The reference framework for ABAC is NIST Special Publication 800-162. See fine-grained data access control.
Step 5: Refuse by default, surface structured errors
When the agent asks about a concept that does not exist, return a structured error that names the unresolved term. Never let the agent retry with guessed alternatives. The agent's job is to ask a follow-up question, not to invent an answer. Refusal is a feature.
Step 6: Capture a point-in-time reproducible audit trail per query
For every query, record the graph version, identity context, resolved entities, proven join paths, and compiled SQL. The audit trail must be replayable months later with the definitions in force at the time of the original query. This is what regulators ask for and what makes Colrows safe in healthcare, financial services, and asset reconstruction. See point-in-time reproducible.
Step 7: Maintain the graph autonomously, approve changes in-product
Schemas drift. Columns are renamed. Definitions evolve. Use autonomous maintenance agents to detect drift and propose updates that humans approve in-product. Manual catalogue updates cannot keep up with AI-agent volume - by the time a steward notices a renamed column, the agent has already returned a thousand wrong answers. See agents that maintain your data systems.
Where does Colrows fit in this checklist?
Colrows ships all seven steps as the default behaviour of the platform. Read the 7-step pipeline walkthrough for the full architectural picture. The same pipeline drives 22,500+ field reps at Cipla, retail-NPA evaluation in BFSI, and 3,000+ travel-retail venues at SSP Group.
What is the smallest first step?
Connect a single datasource, let the graph autonomously build, plug your identity provider in, and watch one query compile through all seven steps. Most teams are running a governed agent within an afternoon.