Agent Observability ≠ Agent Correctness

Monitoring tells you an agent ran. It does not tell you whether it reasoned well. As of Q2 2026, industry surveys report 89% of teams running AI agents have observability tooling, but only 52% have evaluation frameworks.

Why normal software testing breaks down for agents: it assumes determinism (same input → same output). Agents don't hold that property — the same task can complete through multiple valid tool-call paths, and an agent can produce a plausible-looking output through a completely broken reasoning path that no dashboard flags.

Three observability primitives most production teams are missing: trace IDs that follow an agent thread end-to-end, per-tool-call token budgets, and semantic assertion layers that validate outputs before they reach a user (not just log the request/response pair).

Takeaway for a DPM: the fix is upstream — in the semantic infrastructure, data contracts, and governance rules that define "correct" before the agent runs — not just in a dashboard that reports what already happened.