When should organisations prioritise observability over more eval cases for AI agents?

They should prioritise observability when the agent can take multiple tool paths, touch sensitive systems, or create side effects that cannot be predicted in advance. At that point, more examples alone will not close the assurance gap. Runtime telemetry becomes the only reliable way to see what the agent actually did and where it drifted.

Why This Matters for Security Teams

For AI agents, the real assurance problem is not just whether a prompt or workflow can be evaluated on a bench. It is whether the system’s runtime choices, tool calls, and downstream effects are visible enough to support control, investigation, and rollback. That is why observability becomes more important as soon as an agent can touch production systems, make decisions across multiple branches, or interact with secrets and sensitive data.

More eval cases still matter, but they mainly improve confidence in known scenarios. They do not reliably expose novel tool paths, hidden dependencies, or instruction-following failures that appear only under live conditions. Current guidance from the NIST AI Risk Management Framework and the OWASP Agentic AI Top 10 both point toward runtime governance, traceability, and abuse resistance as essential controls for higher-risk systems.

In practice, many security teams discover missing visibility only after an agent has already changed data, invoked an external action, or exposed a privilege path that no eval set ever modeled.

How It Works in Practice

Observability should be treated as the control layer that tells security teams what the agent actually did, while evals tell them what the agent is expected to do. For agentic systems, that means logging the decision path, tool invocations, prompt and context changes, retrieval sources, identity used for each action, and any side effects such as record creation, deletion, or outbound communications. Without that runtime evidence, it is difficult to reconstruct an incident or prove that a guardrail failed for a specific reason.

A useful operating model is to increase observability first when the agent has any of these traits:

• It can choose between multiple tools or workflows.
• It can act on behalf of a user, service account, or non-human identity.
• It can access customer data, secrets, or privileged systems.
• It can trigger changes that are hard to undo.

That is also where frameworks such as the MITRE ATLAS adversarial AI threat matrix and the CSA MAESTRO agentic AI threat modeling framework are useful, because they shift the focus from static test coverage to attack paths, abuse patterns, and runtime controls.

In mature implementations, observability should support correlation across SIEM, SOAR, and application telemetry so that security, platform, and model teams can see the same event with different context. Where possible, the agent should emit structured events for prompt source, retrieval result, tool output, policy decision, and human approval state. These controls tend to break down when agents are embedded in legacy automation chains that do not preserve event identity or action provenance.

Common Variations and Edge Cases

Tighter observability often increases engineering and storage overhead, requiring organisations to balance diagnostic depth against latency, privacy, and operational cost.

There is no universal standard for how much telemetry is enough, so best practice is evolving. For low-risk, single-purpose agents that only draft text or summarise non-sensitive content, extra eval cases may provide more value than extensive runtime logging. For higher-risk agents, however, observability should expand first because the failure mode is usually not a wrong answer, but an unauthorised action, hidden context drift, or an interaction with another system that changes state.

Edge cases appear when an agent operates across regulated workflows, uses delegated credentials, or chains retrieval with external actions. In those environments, the important question is not simply whether the model was correct, but whether its execution can be explained, replayed, and attributed. That aligns closely with the real-world lessons emerging from public incident reporting, including the Anthropic AI-orchestrated cyber espionage campaign report, where runtime behaviour and operator visibility mattered more than lab-only assumptions.

Observability should be prioritised before additional eval depth whenever the agent’s live behaviour can create irreversible side effects, because that is the point where test coverage alone stops being a dependable assurance strategy.

Related resources from NHI Mgmt Group

• When should organisations prioritise posture management for NHIs and AI agents?
• Should organisations prioritise tool scoping or skill governance first for AI agents?
• Should organisations prioritise least privilege or lifecycle governance first for AI agents?
• When should organisations prioritise AI identity governance over new AI deployments?