For decades, the path from a business question to an answer ran through a person. A leader asked a question, an analyst translated it into SQL, the engine ran it, and the answer came back. The analyst was doing more than writing code. They were deciding which table to use, which date range applied, and how to calculate revenue for the team that happened to be asking. That translation was never written down anywhere central. It lived in spreadsheets, onboarding docs, dashboard notes, and people’s heads, and it worked because a human was always in the loop to apply it.
Text-to-SQL tools and agents remove that human. The query path looks almost identical, but the translator is gone, and the translator was doing the work nobody had documented. That removed step is the reason a context layer is now necessary, and it is also the thing that decides who can replace BI with AI and who cannot.
Why the context layer matters
Take a single word: revenue. Ask finance, sales, and product, and you get three different answers. Finance means recognized GAAP revenue, the earned and auditable number on the income statement. Sales means booked ARR, the annualized value of signed contracts. Product means gross billings, the raw amount invoiced before discounts and refunds. None of these is sloppy. Each is correct for the team using it, and each is incomplete without the context that says which one the question intended.
This is not a model problem. Most enterprises are running on the same foundational models, and the model usually reasons correctly. It is just reasoning against the wrong definition of revenue, or making its best guess. A human analyst knows which definition to use, or corrects it on the next pass. An agent picks one without flagging it and runs it across thousands of questions before anyone notices the answer is wrong.
Justin Borgman makes the underlying point in Engineering the AI Leap. Having the data is not the same as having the meaning behind it. Structured data is the operational ground truth of a business, and most enterprises are failing to unlock it. Put another way, if you sat your data in a room by itself, it would not be smart enough to reason. It has no context, no business logic, and none of the institutional knowledge that tells it what the numbers mean.
What an enterprise context layer is
The context layer sits between your agents and your data, and it is both organized and governed. In the Starburst enterprise intelligence platform, it is one of four layers, alongside the analytics engine, the agentic control plane, and the agentic interface. Its job is to apply common definitions, metadata, and policies across every domain, turning raw data into a shared ground truth that both humans and AI can understand.
It does three things. First, the definitions. The metrics and rules that reflect how your business works, not generic ones. Second, meaning. The relationships between those definitions, so agents can reason across domains rather than look up isolated facts. Third, governance. The access controls, audit trails, and traceability applied at the moment context is served, so every answer is auditable back to the definition it used and the person who certified it. That combination is what makes agents trustworthy at scale, not just capable.
How to build one
How do you build an enterprise context layer? There are a few things to keep in mind. First, curate, do not dump. Give an agent too little context and it guesses. Give it too much and it gets distracted by noise. This means that deprecated tables, older definitions, conflicting sources. The right amount of context is curated, specific, and certified. That precision is what separates a trustworthy agent from one that is confidently wrong.
Next, start from data products. A data product is the curated, trusted layer for a dataset, built once for a specific purpose with reusability in mind, so it can be reused by everyone who touches it. That already solved data discoverability for humans. AI raises the bar, because an agent cannot clarify when it is unsure and cannot ask the analyst who left and took the knowledge with them. The metric logic has to be embedded in the product, the definitions have to travel with the data instead of sitting in a wiki page nobody updates, and the business rules have to be encoded rather than remembered.
After that, make data products AI-ready. That means treating them as code, a portable YAML representation carrying the domain, description, owner, and business rules, which plugs into your CI/CD integration so products become versioned and auditable. It also means richer metadata, which means business rules written in plain language, gold standard questions that tell an agent what the product is designed to answer, and metadata enrichment at the table and column level so teams start from a filled-in product rather than a blank slate.
Certify with a human in the loop. Every object gets an AI readiness score. Anything below the threshold stays a draft until a subject matter expert reviews and approves it, and any later edit reverts it to draft automatically. The human retains ownership, and the agent only sees what has been signed off.
Federate what you already have. Most of what you need already exists. Your catalog holds the revenue definition finance signed off on. Your dbt project holds transformation logic the team spent years refining. Your query history knows which joins are valid and which blow up at scale. Building the context layer means harvesting that metadata from existing sources into a steward-approved workflow, not rebuilding it from scratch. This is the same federated approach Starburst applied to data, now applied to metadata and context.
Map it into a semantic graph. Certified entities get linked, metrics connected to their business rules, synonyms, and domain ownership, pre-linked so agents traverse the graph rather than receive a raw context dump. Decision traces and user feedback then feed back into the graph, so the layer learns and evolves with the business instead of going stale. That shared semantic layer is what lets thousands of agents move cleanly across the data estate.
Build on open standards. Federating across catalogs, dbt, and other tools depends on openness, and an open foundation keeps the context layer connected to the systems where meaning already lives. A useful test from the field: if you cannot rip a component out within nine months, you chose the wrong one.
Why the enterprise context layer is worth it
Trust is the return. Agents climb a trust ladder, and the first rung is accuracy. This means that insights only become available on demand, grounded in governed data products and business context, with the use of clear reasoning and cited evidence. Only once accuracy is proven repeatedly do recommended actions, and then automated operations, become safe to delegate with guardrails. Grounding agents in that certified context is what makes the climb possible.
This is what lets AIDA, Starburst’s AI data assistant, reason over governed data products instead of guessing against a raw database, respect your data policies, and explain its logical path to deliver trusted answers in seconds.
It also clears a real backlog. Teams routinely spend over four months building a single dashboard, 51 percent of users say they cannot interact with the data meaningfully, and 72 percent bypass dashboards entirely by exporting raw files to Excel. A context layer that serves trusted, conversational answers retires that wait, which is the core argument for moving beyond static dashboards.
The context layer is the engineered bridge between data you already own and AI you can trust. It is the part of the AI leap that has to be built deliberately, and the enterprises building it are the ones turning AI strategy into immediate business execution.
