Lesson 7 of 10 · Building the context
Testing agent answers before you trust them
Short answer
A readiness project is not finished when the agent connects. You need a repeatable way to check answers, find missing context and catch drift.
By The Lineage Desk · Best Data Catalogs · Published
Desk research from public vendor pages. Editorial assessment.
2 min read
The first demo of an agent on enterprise data is usually impressive. The tenth week is where trust is won or lost. Answers drift as models change, new tables appear, and a definition someone fixed in a dashboard never reaches the agent.
What does an answer-testing loop look like?
It has four steps. Write a set of benchmark questions with known answers. Run the agent against them on a schedule. Compare answers with the expected results. When an answer is wrong, find the missing context and fix it at the source: a definition, a rule, a glossary term.
Which vendors describe this?
Few describe it in detail. Euno describes Context Calibration, which generates benchmark questions, checks agent answers and suggests missing rules and glossary terms. Atlan lists evals and traces as part of activating context for agents. DataHub detects context drift and routes it to subject experts, and cites a customer claim of nearly 2X agent accuracy at Miro. AtScale logs every call and cites a preliminary result of twice the accuracy on the BIRD-Interact benchmark through its MCP server. Alation's AI Governance traces which data an agent touched. The other platforms we scored do not describe answer testing on the pages we read.
Source: Euno blog: AlphaSense x Euno at dbt Summit
Source: Atlan homepage
Source: DataHub Cloud context platform
Source: AtScale: semantic context for AI via MCP
Source: Alation press release: AIOS expansion with six products
How do you write good benchmark questions?
Take them from real work. Pull the twenty questions your analysts answer most often, and add ten that have tripped people up before: a metric with two definitions, a table that was deprecated, a field that depends on personal data. Record the right answer and the reasoning, not only the number. Include at least one question the agent should refuse.
What should you measure?
Track three things over time: the share of questions answered correctly, the share answered with the right sources, and the time from a detected error to a fix in the context. The second matters because a correct number from the wrong table is luck.
Why does this belong in a buying decision?
Vendor accuracy claims come from their own workloads. Your benchmark is the only accuracy figure that applies to your data. Ask each shortlisted vendor to run your questions during the proof of concept, and score the answers yourself.
Keep the benchmark under version control next to your dbt project, so a change to a model and a change to its expected answers can be reviewed together. Treat a failing benchmark question like a failing data test: it blocks the release until someone fixes the context or updates the expected answer with a reason.
The next track turns to buying: how to run that proof of concept and how to read the pricing models.
Key points
- Agent quality decays without a testing loop.
- Build benchmark questions from real analyst work, including refusals.
- Run your own benchmark in every proof of concept; vendor figures are not yours.