Blog
Field notes for teams governing metric logic.
Practical writing on the work behind trusted metrics: authoring, validation, approvals, release artifacts, and the contracts AI and BI systems should inspect before acting.
Editorial Focus
Practical writing for teams turning metric logic into trusted infrastructure.
The ClariLayer blog is organized around the same problem the product solves: business metrics need context before humans, dashboards, and AI agents can trust them. We write for AI transformation leaders, BI and analytics teams, RevOps and finance operators, and analytics engineers who have to make metric definitions reliable without slowing every change to a crawl.
Expect concrete notes on metric lifecycle management rather than broad market commentary. The useful questions are specific: who owns this definition, when was it validated, which version is approved, what evidence should travel with a release, and what should an AI agent do when a user asks for a deprecated metric framing?
Metric governance with evidence
How teams turn definitions into owned, approved, versioned assets instead of loose dashboard logic. We connect the editorial topic to approval workflows, managed variants, release evidence, and the reasoning trail behind each definition.
Read about metric governance workflowsAI agents and governed context
Why agents need governed metric contracts before they answer revenue questions, draft SQL, or trigger workflows. The useful questions are concrete: what context an agent needs, where it should come from, and how a deprecated metric framing should be refused.
Read about the Contract API for AI agentsWarehouse-backed trust
The gap between a written metric definition and logic that still runs against live data. We cover validation probes, catalog grounding, deployment and rollback boundaries, and the current caveat that Observe/query-history ingestion remains Databricks-only.
Read about warehouse validation evidenceStart Here
Follow the internal links from an idea to the product surface.
Each article index path points back to substantive product pages so readers can move from editorial framing to the evidence model: features for the lifecycle, use cases for concrete operating pain, and docs for the API contract used by downstream systems.
New to ClariLayer
Start with the feature overview to understand the context layer: authoring, validation, governance, registry discovery, and API access working together.
Open ClariLayer feature overviewComparing active problems
Use the scenario page when you are mapping a concrete pain point, such as dashboard disagreement, shadow BI, documentation drift, or AI agents acting on unapproved definitions.
Open metric trust use casesPlanning implementation
Technical readers can review the public contract shape that downstream systems use to retrieve governed metric definitions and trust signals.
Open Metrics Contract API documentation
Your AI Agent Used a Retired Metric Definition. Did It Tell You?
Across 9,000 single-turn SQL questions, ClariLayer's governed envelope produced canonical-with-rejection on 297/360 Drift calls (82.5%) vs 0-1 across the four non-governed baselines.
The ClariLayer Trust Benchmark v1: A 2,136-Call Study of AI Accuracy
AI agents writing SQL against your warehouse get definitional questions wrong 91-99% of the time. We built an 89-question benchmark to measure it.
The Context Gap: Why Warehouses and Semantic Layers Aren't Enough
Your warehouse computes numbers. Your semantic layer queries them. But who governs what metrics mean? Meet the context layer — the missing third layer.
What ClariLayer Does (And What It Does Not)
ClariLayer is not a warehouse, not a semantic layer, and not a wiki. It is the context layer — the missing piece that captures meaning, ownership, and trust for business metrics.
Why AI Agents Need a Context Layer
AI agents are making autonomous decisions based on metric definitions. But no tool captures the business context they need to act responsibly. This is the context layer gap.
Why Your Metrics Need a Context Layer
Data warehouses tell you how a number is computed. But your AI agents need to know what it means, who owns it, and whether they should trust it.