Blog
Field notes for analysts who live in their AI.
Practical writing for the individual analyst working in Claude Code, Cursor, or Codex against their own data — recall and remember, reconciling numbers that disagree, and context that compounds the longer it rides along.
Editorial Focus
Practical writing for the analyst whose AI keeps forgetting their data.
The ClariLayer blog is organized around the same problem the product solves: your AI agent starts every session knowing nothing about your data. We write for the individual analyst working in Claude Code, Cursor, or Codex against their own — often messy, ungoverned, cross-source — warehouse data. No data team, no platform account, no procurement. Just you, your data, and the agent you already work in.
Expect concrete notes from inside the agent rather than broad market commentary. The useful questions are specific: which table is canonical, which join is the right one, why these two numbers don’t match, and how to make your agent stop repeating the wrong-table, wrong-join, refunds-in-revenue mistake you already corrected last week.
Context that survives the next session
Why every fresh agent chat starts from nothing, and how recall and remember let your context persist across sessions. The useful questions are concrete: which table is canonical, which join is right, what your agent should stop getting wrong about your data.
Read about how recall and remember workReconciling numbers that disagree
The gap between a saved definition and what your warehouse actually returns. We cover the first reconcile — "why don't these two numbers match?" — how a mismatch surfaces as a caveat, and why ClariLayer never holds your warehouse credentials or runs SQL itself.
Read about the reconcile momentWorking in Claude Code, Cursor, and Codex
Practical notes for the analyst who already lives in an AI agent against their own data. Bootstrapping from the SQL you already have, the MCP install, and the corrections that compound the longer the context layer rides along.
Read about the quickstartStart Here
Go from an idea to the agent you already work in.
Each path points back to a substantive product page so you can move from editorial framing to actually connecting ClariLayer: features for what the context layer does, use cases for the moments you recognize, and the quickstart for installing it into your agent.
New to ClariLayer
Start with the feature overview to understand the context layer: recall, remember, reconcile, and bootstrap working together inside the agent you already use.
Open the ClariLayer feature overviewRecognize the moment
Read the single-player use cases when you are living a concrete pain: re-explaining your data every session, two numbers that won't reconcile, or an agent that keeps making the same data mistakes.
Open the single-player use casesConnect your AI
Ready to install? The quickstart walks through adding ClariLayer as an MCP server to Claude Code, Cursor, or Codex and bootstrapping from the SQL you already have.
Open the quickstartAnthropic and OpenAI both said context is the bottleneck for data agents. Here's what they didn't say.
Anthropic and OpenAI both concluded the bottleneck for data agents is context, not SQL generation. Field notes from building past the failure modes they describe — for the analyst with no data team.
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.