Runtime behaviour matters because agents make decisions dynamically, so the risk is created in execution, not only in setup. A static scan can confirm policy at a point in time, but it cannot see an agent that changes tool usage, expands access through delegation, or reaches sensitive data between scans. Governance has to move to the moment of action.
Why execution time is the real control point for agentic AI
Runtime behaviour is where an agent actually decides, delegates, calls tools, and touches data, so it is the point at which risk becomes real. A configuration scan can tell you what was approved at a moment in time, but it cannot reliably show whether the agent later widened scope, chained actions in an unsafe order, or used an access path that was never obvious in the initial setup.
That is why the question is not just whether the agent was configured correctly, but whether its live behaviour stays inside the approved decision boundary. In agentic systems, execution-time policy, observability, and intervention matter because the dangerous state may exist only briefly, and static posture checks often miss that window.
What static scanning misses in a dynamic agent flow
Configuration scanning is useful for finding obvious misconfiguration, but it has a narrow field of view. It can confirm declared settings, allowed tools, or baseline permissions, yet it does not prove how the agent behaves when prompts change, context shifts, or a tool result steers the next action. That gap matters most when a harmless-looking setup becomes risky only after a chain of runtime decisions.
AI Agents vs Agentic AI is a useful reminder that autonomy changes the security problem as soon as the system can choose actions rather than merely display output. The more the system can plan, select tools, and continue without fresh human review, the more the security question shifts from static approval to live control.
The practical consequence is that you need to validate not just the intended configuration, but the actual sequence of actions. A scan may say the policy exists, while runtime telemetry shows the agent selecting a new tool, invoking a broader scope, or reaching a sensitive resource through an allowed but unexpected path.
How to govern behaviour at the moment of action
For agentic ai, the strongest control is the one that can decide per action, not just per deployment. That means checking the current principal, the current request, and the current destination before a tool call is allowed to proceed. It also means treating delegation, step-up access, and session context as live security state, not as one-time setup details.
AI Agent Authorisation Guide fits this problem directly because it focuses on least privilege, task-scoped access, and per-action decisions. If the access decision is made at runtime, then the agent can be stopped from escalating simply because its initial configuration looked acceptable.
AI Agent Observability, Audit and Incident Response Guide adds the missing operational layer: log the action, attribute it to the agent, and detect when the behaviour diverges from the expected baseline. Without that evidence, teams often discover drift only after the agent has already crossed a boundary.
Risk and Threat Considerations
Runtime-first governance reduces the chance that an agent can accumulate access, misuse a tool, or reach data in a way the original scan never anticipated. The main risk is false confidence: a clean configuration report can hide a live execution path that is only visible when the agent is actually acting.
Failure mechanism: The agent changes behaviour at execution time through prompt influence, delegated authority, chained tool use, or contextual access that was not obvious in the original configuration. A static scan cannot observe that live decision path, so the control fails at the exact moment the risk is created.
Impact: Sensitive data exposure, unauthorized action, privilege expansion, and delayed detection become more likely, especially when the system is trusted to act repeatedly between review cycles. The result is not just a missed misconfiguration, but a missed opportunity to stop the harmful action before it happens.
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 and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Runtime behaviour can expand delegated access or misuse privilege during execution. |
| ASI02 — Tool Misuse | The question centers on tool choices and unsafe actions that appear only during execution. | |
| ASI08 — Cascading Failures | Live agent chains can create downstream failures that static scans do not reveal. | |
| Recommendation — Enforce per-action authorization and block privilege expansion at runtime. Validate each tool call against current policy before execution. Add runtime containment and stop conditions for multi-step agent actions. | ||
| NIST AI RMF | GOVERN — Govern | Agentic AI needs governance that covers live decision-making and oversight. |
| Recommendation — Define runtime oversight, accountability, and escalation rules for agent actions. | ||
| NIST SP 800-53 Rev 5 | AU-6 — Audit Review, Analysis, and Reporting | Runtime behavior needs action logs and review to detect drift and abuse. |
| Recommendation — Review agent audit data for unexpected tool use and authorization changes. | ||
Practitioner Guidance
What to prioritise: Put runtime policy enforcement and action logging ahead of periodic posture checks for any agent that can call tools, access data, or act on behalf of a user. If the agent can change what it does after startup, configuration scanning is only a baseline, not a control.
What to verify: Confirm that every high-impact action is checked at decision time against the current identity, scope, and approval state, and that those decisions are observable after the fact. The useful test is whether you can explain why a specific action was allowed, not just whether the deployment was approved.
Common mistake: Treating a secure initial configuration as proof that the runtime is safe. In agentic systems, the interesting failure is often the second or third action, when the agent has more context than it had at setup.
Practitioner takeaway: For agentic AI, security has to follow the action, because the risk is produced by what the agent does in the moment, not by what the configuration looked like at rest.
Related resources from NHI Mgmt Group
- When does just-in-time access reduce risk for agentic AI, and when does it fall short?
- How should security teams govern machine identity credentials in agentic AI environments?
- How should security teams use AI in secret scanning without creating new blind spots?
- How should security teams govern agentic AI access to secrets without losing visibility into runtime behaviour?
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Reviewed and updated by the NHIMG editorial team on October 12, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org