Resources
Guides
Practical playbooks for evaluating, governing, and shipping AI on enterprise data. Each guide covers one decision in depth - from buyer's guides to step-by-step checklists.
The Best Semantic Layer for AI Agents in 2026
An honest comparison of every major semantic layer in 2026 - and the five criteria that actually matter for AI workloads.
Read guideHow to Add Governance to AI Agents: A 7-Step Checklist
The 7 things you have to ship to make an enterprise AI agent safe to put in production.
Read guideHow to Prevent AI Hallucinations on Enterprise Data
The structural fix: typed semantic graph + constrained planning + join path proof + compile-time refusal.
Read guideWhich guide do you need?
Each guide covers one decision end to end, written for the people who own it - CDOs, data platform leads, and AI engineering leads at regulated enterprises.
Choosing a vendor
Start with the buyer's guide - every major semantic layer compared on the five criteria that decide AI workloads - then pressure-test your shortlist with the 40-question evaluation checklist.
Shipping an agent to production
Work through the 7-step governance checklist: identity-pinned calls, compile-time governance (RBAC + ABAC + row/column-level predicates), refusal-by-default, and a point-in-time reproducible audit trail.
Fixing wrong answers
If your agent already ships but answers drift, read the hallucination-prevention guide: a typed semantic graph, constrained planning, join path proof, and compile-time refusal.
New to the category? Ground yourself first with What is a semantic layer? and how the 7-step pipeline works, or see how the platforms compare.
Looking for narrative essays instead?
Browse the Colrows blog for field reports, technical deep-dives, and product perspectives.