Topic starter
02/08/2026 12:15 pm
TL;DR: AI agent evaluation is moving from niche testing to a broader governance requirement as the market is projected to grow from $0.55 billion in 2025 to $2.05 billion by 2030, according to Openlayer. The core issue is not output quality alone but whether teams can trace tool use, session state, and policy violations before agents reach production.
Editorial analysis by NHI Mgmt Group, based on content published by Openlayer: “Best AI Agent Evaluation Platforms (Feb 2026)”.
Key questions
Q: How should security teams govern AI agents that can choose tools at runtime?
A: Security teams should govern runtime agent choice as an access event, not as a simple application action.
Q: Why do AI agents create more risk than standard LLM evaluations suggest?
A: AI agents create more risk because the dangerous part is often what they do after the response is generated.
Q: What are the signs that AI agent observability is failing?
A: The main signs are missing tool metadata, disconnected traces between agent hops, unexplained retries, and an inability to reconcile a successful call with the actual state change it caused.
Practitioner guidance
- Implement full-session tracing for every production agent Capture tool invocations, retries, branching decisions, and state changes so you can reconstruct the full action path after an incident or failed test.
- Block high-risk tool actions at runtime Enforce policy-based guardrails for prompt injection, PII leakage, and unsafe external calls before the agent reaches downstream systems.
- Test delegated tool access before production release Run behavioural tests that verify which tools an agent can select, how it handles errors, and whether it continues after policy-relevant failures.
Bottom line: AI agent evaluation is moving into governance because multi-step systems can misbehave in the path between actions, not only in the final answer.
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02/08/2026 1:18 pm
AI agent governance is becoming a runtime control problem, not a model-quality problem. The article shows why output scoring alone is insufficient once a system can choose tools, retain state, and act across multiple steps. That shifts the governance question from whether an answer is good to whether the session remained within authorised bounds. Practitioners should treat agent runtime behaviour as a control surface, not a by-product of model performance.
A few things that frame the scale:
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: Should teams use OWASP-style guardrails or runtime controls for AI governance?
A: They need both, but not as substitutes. Guardrails help define which risks matter and what is disallowed, while runtime controls enforce access, containment, and escalation boundaries inside the live system. If a model can still act, retrieve, or disclose data outside policy intent, runtime control is the deciding layer.
👉 Read our full editorial: AI agent evaluation tools expose the governance gap in autonomous systems