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Discovery Found Everything: Your Agent Still Doesnt Know What Matters | TestMu 2026

Discovery Found Everything: Your Agent Still Doesnt Know What Matters | TestMu 2026

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About the talk

I have an AI service desk analyst agent running in production right now. It completes work on its own, without a person confirming it. It is also restricted to one incident type on one service, and this session is about why.

The agentic testing conversation has focused on one question. Is the agent reasoning correctly? We build evaluation suites, we score outputs, we trace decisions. What we have not examined is the quality of the enterprise context the agent reasons over, and the assumption underneath it that a populated CMDB means an informed agent.

That assumption deserves scrutiny, but not the kind it usually gets. Where automated discovery is running, the asset layer is often excellent. Discovery runs on a schedule and finds what is actually deployed. The CMDB is accurate about what exists. It is far less reliable about what those things mean to the business. Service mapping is a project rather than a scheduled job, and most organizations never finish it. Criticality was entered once at build time. Ownership drifts after every reorganization. Failure behaviour is discovered during outages and rarely written down. Every layer carrying meaning is human-maintained, and nothing forces it to stay correct.

This was survivable while humans read the CMDB, because experienced people silently correct it as they go. An agent cannot. It reads the record and accepts it exactly as written. We chose our first use case the way everybody does, on ticket volume, and it was the right decision. But volume told us how often the work happens. It told us nothing about whether our data could support an agent doing it. We got a good outcome by coincidence, and the next use case is where that stops working.

Drawing on two decades building and running configuration management, change governance and testing in an organization where a failed change is a public service disruption, this session offers a four-part Context Integrity Test that fits inside the change process you already run.

Key Takeaways:

Why an accurate CMDB and a trustworthy CMDB are different things, and why discovery solves the first and not the second.

The four layers that carry meaning rather than inventory, and how each one degrades.

Why choosing an agent's first use case on volume works, and why it fails for the second one.

A four-part Context Integrity Test, and how to match an agent's permission level to the data quality of the specific service it touches.

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