Explainer
What is an evidence-backed context layer?
A context layer sits between enterprise data and the AI agents that use it, and turns raw data into meaning an agent can act on. An evidence-backed context layer goes one step further: every fact in it can show where it came from.
The idea
Context is what turns data into an answer
An AI model brings intelligence. It does not bring knowledge of your business: which revenue figure the board means, which table is the trusted one, what a field in a forty-year-old program actually holds.
A context layer supplies that knowledge. It records where data comes from and where it flows — its lineage — what data, transformations and business concepts mean, and how far each of those facts can be trusted. Agents read it when they reason, so that the answer they give is the one your business would give.
Most context layers stop there. An evidence-backed context layer also attaches the proof: behind every fact sits the source it was derived from, so a person, an auditor or another agent can check it rather than take it on trust.
Why it matters now
Agents fail for lack of context, not intelligence
Models are converging. The context they reason with is what makes an answer yours — and what makes it right.
An agent that writes flawless SQL will still report the wrong revenue if nobody told it which revenue the business means. The answer reads as correct and nothing in it says otherwise — which is why this failure is so costly. In a June 2026 VentureBeat survey of 101 enterprises, 57% had traced a confident but wrong agent answer to missing or inconsistent business context.
In regulated industries the bar is higher still. A right answer is not enough; you have to be able to show how the figure was made, whether a person or an agent produced it.
Where context comes from
Three sources — and only one says what the system does
Every context layer is built from somewhere. Where it comes from decides what it can prove.
Curation
What people say data means: glossaries, policies, documentation. Valuable, but it records intent rather than behaviour, and it drifts the moment the code changes and nobody updates the page.
Usage
How data is used: query history, dashboards, the joins analysts tend to write. It learns behaviour rather than authority — a popular query can be popular and wrong.
Making
How data is actually produced: the code, configuration, copybooks, documents and logs behind every figure. It is the only source that says what the system does, which is why Semantic Scout builds from it.
Trust
What makes context trustworthy
An answer with a file and a line behind it survives an audit. A confident answer with nothing behind it does not.
- Evidence on every fact — each one cites the file and line it came from.
- Current — checked against the source on every read, so stale context is known to be stale.
- Complete, and where it isn’t, the gap is named rather than hidden.
- Accurate — measured against graded cases, including cases withheld from development.
- Disagreement visible — when sources contradict each other, you see it instead of a guess.
Reach
Context that covers the whole estate
Context that stops at one platform, or at the warehouse, leaves an agent guessing about everything upstream.
Most enterprises run several data platforms, and the large Nordic banks still capture much of their data on the mainframe. A figure in a regulatory report may pass through all of them. An evidence-backed context layer follows it the whole way — mainframe and modern platforms in one lineage, rather than separate tools stitched together at a report boundary — and shows the path a value actually takes.
For agents
How agents use it
Agents query the context directly, the same context your people explore.
Agents reach Semantic Scout’s context over MCP and A2A, the open protocols agents use to call tools and each other. What comes back carries its evidence, so an agent’s answer can be traced the same way a person’s can. And when an agent needs context that is missing or stale, it can ask for it instead of reasoning from whatever it happens to find.
Questions
What is the difference between a semantic layer and a context layer?
A semantic layer standardises metric definitions so that tools compute the same numbers. A context layer is broader: it adds where data comes from, what it means across systems, and whether it can be trusted right now. An evidence-backed context layer also shows the source behind each fact.
Is a data catalog a context layer?
A catalog documents what exists, largely for people to read. A context layer serves meaning to agents at the moment they reason, in a form they can act on. Semantic Scout is not a catalog replacement.
My data platform already has a context layer. Do I need another?
A platform’s own context sees what runs on that platform, and learns largely from how data is used there. Most estates span several platforms — and, in the large Nordic banks, a mainframe. Semantic Scout builds context across all of it, from the code that produces the data, with evidence on every fact.
See your own context, built from your own code
A 30-minute demo shows the context layer. A pilot builds yours, from your own repositories.