Why do AI agents complicate performance comparisons between security tools?

Why This Matters for Security Teams

AI agents change the unit of measurement. A single user request can trigger tool calls, retrieval, retries, memory updates, policy checks, and external actions, which means security telemetry grows far faster than the visible application transaction. That makes raw cost-per-request, event counts, or latency figures easy to misread unless the measurement boundary is explicit.

This is not just a performance problem. It affects procurement, tuning, and incident response. A tool that inspects only application-level calls may look efficient because it sees fewer events, while a control embedded closer to the runtime, kernel, or identity layer may absorb far more activity and provide stronger containment. Current guidance in the NIST AI Risk Management Framework and the OWASP Agentic AI Top 10 both point toward measuring risk and behaviour in context, not relying on a single superficial metric.

For NHI and identity teams, the same issue applies to agent credentials, token usage, and delegated authority. If the comparison ignores where identity is enforced, it can reward tools that undercount the real security work. In practice, many security teams discover this only after an agent has already generated noisy telemetry, unexpected tool usage, or an expensive incident review rather than through intentional benchmark design.

How It Works in Practice

Comparing tools around AI agents requires defining what is being measured: application events, runtime actions, identity assertions, or enforced policy decisions. Those are not interchangeable. A process-level sensor might only observe API calls from the application, while a lower-level sensor can see file access, network egress, child processes, and execution paths that an agent uses after receiving a prompt or retrieval result. The lower the observation boundary, the more likely the tool is to capture the agent’s real blast radius.

That is why performance comparisons should include both security coverage and operational overhead. Useful comparisons usually separate:

• event volume generated by the agent
• time to detect or block harmful behavior
• identity and secret exposure during tool use
• false positives created by multi-step autonomous workflows
• resilience when the agent retries, branches, or chains actions

Frameworks such as MITRE ATLAS adversarial AI threat matrix and the CSA MAESTRO agentic AI threat modeling framework are useful because they push evaluation toward attack paths, abuse cases, and control points rather than isolated throughput numbers. The practical question is not only “how fast is the tool” but “what layer does it protect, and what agent behaviour can still slip through?”

Where agents use retrieval, browser actions, code execution, or external APIs, the comparison must also account for inherited trust. A tool that watches the prompt may miss the malicious action that happens three steps later. NHI controls matter here because the agent’s service account, secrets, and delegated permissions often determine the true risk boundary. These controls tend to break down when one benchmark mixes synthetic prompts, real credentials, and different enforcement layers because the tools are no longer observing the same security problem.

Common Variations and Edge Cases

Tighter measurement often increases overhead, which means organisations have to balance benchmark simplicity against security realism. That tradeoff becomes sharper when agents are allowed to call multiple tools, maintain memory, or act asynchronously across sessions. In those cases, a simple per-request comparison can hide the cost of delayed actions, chained prompts, or state carried from one interaction to the next.

Best practice is evolving for how to compare agentic systems fairly. There is no universal standard for this yet, but current guidance suggests normalising results by security-relevant outcome rather than by raw event count alone. For example, one environment may care about blocked tool calls, another about credential misuse, and another about prompt injection resistance. The same vendor or control can look stronger or weaker depending on which outcome is being measured.

That is why the strongest comparisons usually combine governance and control frameworks such as the NIST AI Risk Management Framework, the OWASP Top 10 for Agentic Applications 2026, and the NIST Cybersecurity Framework 2.0. That combination helps teams compare tools by trust boundaries, detection depth, and operational resilience rather than by a single attractive metric.

The biggest edge case is mixed estates where traditional applications, LLM workflows, and autonomous agents share the same telemetry pipeline. In those environments, per-event comparisons become unreliable because the baseline workload is fundamentally different and the agent’s security behaviour is distributed across multiple layers.

Related resources from NHI Mgmt Group

• How should security teams reduce risk from AI agents and developer tools that use secrets locally?
• Why do AI agents complicate traditional security reporting?
• What is the difference between API-key security and hardware-bound identity for AI agents?
• How should security teams test AI agents that can call tools and APIs?