What breaks when AI evaluation does not include agent trajectories and tool-call telemetry?

You lose visibility into how the agent reached its result, so failures in tool choice, argument construction, repeated actions, stale memory, and termination logic can look like ordinary output drift. Without span-level telemetry, you cannot reliably score the path, reproduce the failure, or detect loop patterns that burn tokens and end in an incorrect state.

Why evaluation breaks down without trajectories and telemetry

Agent evaluation is not only about the final answer. Once you remove the intermediate trajectory, you lose the evidence needed to tell whether the model succeeded through sound reasoning or by accident. The result is weaker diagnosis, because output-only scoring cannot separate a clean path from a brittle one that happened to land on the right text.

That matters most for tool-using agents, where correctness depends on state transitions, action ordering, and argument quality. An answer can be fluent while the path contains the real defect, such as choosing the wrong tool, constructing a malformed call, or looping through the same action until the budget is exhausted.

For agent systems that need decision-level governance, the path is part of the control surface. NHIMG’s Agentic AI Security Guide is useful here because it treats tools, memory, orchestration and identity as separate failure planes, which is exactly why evaluation needs span-level visibility rather than a single end-state score.

What you can no longer measure or reproduce

Without agent trajectories and tool-call telemetry, you cannot reliably reconstruct why a run failed. That blocks path scoring, root-cause analysis, regression comparisons, and any attempt to tell whether a new prompt, tool schema, model update or policy change improved behaviour.

Telemetry also gives you the only defensible way to identify repeated actions, termination failures, stale-memory usage, and bad tool selection patterns. A final answer may look like ordinary model drift, but the underlying defect can be an execution problem that only appears in the sequence of calls.

For teams evaluating autonomy and access boundaries, this is where AI Agent Observability, Audit and Incident Response Guide becomes directly relevant, because attribution, logging and incident response all depend on being able to see what the agent did, not just what it finally said.

Why hidden loops and bad tool use become operational risk

When the evaluation stack ignores call-level behaviour, loop patterns can survive in production because they are invisible in aggregate output. That creates unnecessary token spend, latency spikes, and repeated side effects if the agent replays tool actions against live systems.

It also hides the distinction between a wrong answer and an unsafe process. A tool call can fail in a way that is recoverable, but if the evaluator never sees the call sequence, the system may be tuned toward better-looking text instead of safer execution. In agentic systems, those are not the same objective.

NHIMG’s Zero Trust for AI Agents is a useful companion concept because per-action verification only works if every action is observable enough to validate the principal, the request and the resulting effect.

Risk and Threat Considerations

Opaque evaluation creates a blind spot where unsafe agent behaviour can be mistaken for benign model noise. That increases the chance that tool misuse, repeated execution, or incorrect termination survives review and reaches production systems.

Failure mechanism: The evaluator sees only the end result, so it cannot distinguish a correct final state from a wrong sequence of tool calls, looped actions, or stale-memory decisions that happened to end in a plausible output.

Impact: Teams miss regression signals, undercount failure rates, and may ship agents that waste tokens, execute unintended actions, or repeat the same bad path until they hit a cost or reliability limit.

Standards & Framework Alignment

This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.

OWASP Agentic AI Top 10 addresses the attack and risk surface, while NIST AI RMF sets the governance and control requirements practitioners need to meet.

Framework	Control / Reference	Relevance
OWASP Agentic AI Top 10	ASI02 — Tool Misuse	Tool-call telemetry is needed to detect and score tool misuse in agent runs.
	ASI08 — Cascading Failures	Missing trajectories hide loops and repeated actions that can cascade into bad states.
Recommendation — Trace tool choices and arguments to catch misuse before release. Instrument runs to surface repeated actions and failure cascades.
NIST AI RMF	GOVERN — Govern	Evaluation telemetry supports AI governance, accountability and measurable oversight.
Recommendation — Define evaluation telemetry requirements as part of AI governance.

Practitioner Guidance

What to verify: Make sure your evaluation records the full run trace, including tool name, arguments, timestamps, return values, retries, termination signals and memory reads or writes. If you cannot replay the run from telemetry, you do not have enough evidence to trust the score.

Decision rule: If the metric only judges final text, treat it as a completion score, not an agent-quality score. For tool-using systems, require path-aware evaluation before you use the result for model selection, release gating or incident review.

Practitioner takeaway: The dangerous failure mode is not merely a wrong answer, it is an unobserved wrong process. If you cannot inspect the trajectory, you cannot reliably govern, reproduce or improve the agent.