What should security teams measure first in agentic workflows?

Measure the Path, Not Just the Output

Security teams should begin with measures that reveal how the agent actually executed, because a polished final answer can hide unsafe routing. The first question is whether the workflow made safe choices at each step, not whether the final artifact looks acceptable. That means prioritising trajectory-level signals that expose decision quality, control points, and recovery behaviour.

Execution-path measures are especially important in agentic workflows because risk often accumulates before the final output. Tool selection, argument construction, handoffs, looping, and recovery all change whether a workflow stays within bounds, so the earliest metrics should tell you where the agent drifted, stalled, or crossed a trust boundary.

For teams building out observability, an AI Agent Observability, Audit and Incident Response Guide is the right starting point for deciding what to log and how to attribute actions across the workflow.

What to Treat as First-Order Failure Modes

The first measures should distinguish between a workflow that completed and a workflow that completed safely. Tool selection tells you whether the agent chose a permissible capability; argument validity shows whether the tool was used with correct, bounded inputs; handoff quality shows whether state, intent, and approvals survived transitions; looping shows whether the workflow became unstable or wasteful; and recovery shows whether the system detected and corrected failure before harm spread.

Those are first-order because they are closer to the risk boundary than output quality. A task can produce the right-looking result while still violating policy, overstepping scope, or depending on unsafe intermediate actions. Measuring only the end state creates blind spots around excessive agency, hidden retries, and broken escalation paths.

For the broader risk model, the Agentic AI Security Guide is useful because it frames the main failure surfaces across inputs, tools, orchestration, and identity, rather than treating the agent as a single black box.

How to Turn Those Signals into a Usable Baseline

Security teams should baseline the workflow at the trajectory level before they optimise for success rate or user satisfaction. The practical unit of analysis is the run: what tools were chosen, what arguments were passed, what approvals or handoffs occurred, where retries happened, and whether recovery was automatic, delayed, or absent. That gives you a stable measurement model for comparing different prompts, policies, models, and task types.

If the workflow includes delegated authority or task-scoped access, measure whether the agent stayed within the intended action envelope at each step. The most useful first baseline is the one that can answer whether a run remained bounded, not merely whether it finished. AI Agent Authorisation Guide is a strong companion when you need to connect trajectory signals to per-action permissions and approval gates.

Practitioner Guidance

What to prioritise: Start with metrics that can explain unsafe execution, not just successful completion. If you can only instrument a few signals first, choose the ones that expose tool choice, invalid arguments, handoff breakage, repeated loops, and recovery after a failure.

What to verify: Make sure each metric is tied to a specific decision point in the workflow and not just a generic log count. Good trajectory measurement should let you reconstruct why the agent took a path, where it lost context, and whether a control actually intervened.

Common mistake: Treating answer quality as the main score leads teams to miss the highest-risk runs, especially when the agent arrives at the right outcome through an unsafe or over-privileged route.

Practitioner takeaway: The first useful measurements are the ones that show whether the agent stayed safe while acting, because that is where trajectory risk lives and where output-only evaluation fails most often.

Related resources from NHI Mgmt Group

• How should security teams govern machine identity credentials in agentic AI environments?
• How should security teams manage permissions for AI agents?
• How should security teams govern AI agents that use OAuth access?
• How should security teams limit the risk from AI agents that have access to production systems?