Your Agent Still Doesnt Know What Matters [Testμ 2026]

At Testμ Conf 2026, Toronto Transit Commission governance leader Angela Cutulenco argued that modern CMDBs are often accurate at discovering technical assets but remain unreliable as sources of business context, such as ownership, service mapping, criticality, and dependency behavior. She described these as two databases sharing one interface: automated discovery continually updates facts about infrastructure, while human-maintained meaning can become incomplete or stale without any mechanism to detect drift. This gap is usually masked by experienced engineers who silently correct records from organisational knowledge, but AI agents cannot distinguish missing data from deliberate values or infer undocumented failover arrangements and role changes. Using hypothetical access-administration failures, she maintained that an agent may act logically on correct technical records yet still produce harmful outcomes because the underlying business context is outdated. Rather than recommending a long-term effort to fully repair the CMDB before using AI, Cutulenco proposed matching agent autonomy to the integrity of the available context, limiting uncertain systems to read-only or recommendation roles while assessing whether service maps, ownership, criticality, and incident history provide a sufficiently reliable operational picture.

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