Yes, when the agent can act quickly and repeatedly on behalf of users. Human review still has a role for exceptions and investigations, but the primary boundary has to move to execution time if the system is expected to stay both useful and safe.
Why runtime intent checks belong at the execution boundary
agentic ai changes the control point. When a system can take multiple steps, chain tools and act quickly, the meaningful safety boundary is not “did a human approve the idea at some earlier point?” but “does this specific action still fit the current intent, policy and context at the moment it executes?” Runtime checks matter because agent behaviour can diverge after planning, context changes, tool output arrives, or a prompt has been manipulated.
That is why intent validation needs to happen where the action is actually about to leave the system. A human review loop is too slow for routine, repeated, low-latency decisions, and it does not scale when the agent is operating continuously. The stronger pattern is to treat human review as a fallback for edge cases, exceptions and investigations, not as the primary boundary for every action.
For a broader view of where agent autonomy changes the security model, see AI Agents vs Agentic AI. For systems that actually need to act on behalf of a user, the identity and delegation model should be explicit, as described in Agentic AI Identity Guide.
What runtime intent checks should actually verify
A good runtime intent check is not a vague “are you sure?” prompt. It should verify whether the requested action still matches the authorised purpose, whether the target, scope and parameters are acceptable, and whether the agent is about to cross a policy boundary that was not approved for this run. In practice, this often means checking the current task, the current principal, the current data exposure and the current tool or resource being touched.
The check should also be narrow enough to be enforceable. If the agent can send messages, make purchases, modify records, call APIs or deploy code, the policy should evaluate those actions separately rather than bundling everything into one broad approval. That lets routine work flow automatically while forcing a stop at genuinely sensitive steps. If you want a structured authorisation view of that pattern, AI Agent Authorisation Guide explains how per-action decisions, task-scoped access and delegated authority fit together.
Runtime intent checks are also most useful when the system can explain its own decision path. If the agent cannot justify why the action matches the user’s intent, or if the evidence comes from stale context, the safe choice is to block, degrade or escalate. That is much more operationally useful than waiting for a person to inspect every action after the fact.
Where human review still adds unique value
Human review is still important, but for different jobs. It is strongest when the system encounters ambiguity, unusual blast radius, novel tool combinations, policy exceptions or suspected compromise. It is also the right layer for sampling, investigation, policy refinement and post-incident learning. Those are judgement-heavy tasks where context is richer than any single runtime gate.
What human review should not be doing is serving as the default control for a high-frequency agent loop. If every meaningful step waits on a person, the agent stops being operationally useful, and reviewers become bottlenecks who are asked to rubber-stamp decisions they cannot fully inspect in time. A better balance is to reserve human intervention for exceptions, while machine-enforced checks govern the ordinary path.
That trade-off is easier to manage when the agent’s permissions are already constrained. For example, a strong runtime boundary works best when the agent’s access is limited to what the current task needs, and when the system can revoke or narrow access without breaking the whole workflow. The practical design pattern is continuous control, not continuous supervision.
Risk and Threat Considerations
When organisations rely too heavily on human review, the main risk is control lag: the agent can complete harmful or unintended actions before a reviewer ever sees the request. That is especially dangerous when the agent can act repeatedly, chain permissions, or operate during off-hours without friction.
Failure mechanism: An attacker, poisoned prompt, stale context, or overly broad delegation can steer the agent into a valid-looking but unsafe action path, and a delayed human approval loop may arrive after the exposure has already happened.
Impact: The organisation can get silent overreach, data exposure, unauthorised transactions, or rapid multi-step misuse at machine speed, with review only discovering the issue after the blast radius has expanded.
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 and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST AI RMF, NIST SP 800-53 Rev 5 and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Runtime intent checks reduce unsafe privilege use at action time. |
| ASI02 — Tool Misuse | The question is about stopping harmful tool actions at execution time. | |
| ASI09 — Human-Agent Trust Exploitation | Human review can be bypassed or delayed when trust is misplaced. | |
| Recommendation — Enforce per-action policy checks before the agent exercises privilege. Gate each tool invocation against current intent and policy. Require runtime verification when agent output could exploit user trust. | ||
| NIST AI RMF | Govern | The question concerns AI governance decisions about when to rely on humans versus runtime controls. |
| Recommendation — Define accountability for runtime intent checks and escalation paths. | ||
| OWASP Non-Human Identity Top 10 | NHI-05 — Overprivileged NHI | Runtime checks matter most when agent authority could exceed task needs. |
| NHI-04 — Insecure Authentication | Runtime checks depend on knowing which principal is acting at the moment of execution. | |
| Recommendation — Limit agent privilege to the minimum task scope and recheck it at execution. Verify the active agent identity before allowing sensitive actions to proceed. | ||
| NIST SP 800-53 Rev 5 | AC-6 — Least Privilege | The answer centers on constraining agent actions to what is needed now. |
| AU-2 — Event Logging | Runtime checks and exception handling need auditable execution records. | |
| IA-9 — Service Identification and Authentication | Agent action control depends on authenticating non-human principals correctly. | |
| Recommendation — Apply least privilege so the agent can only execute approved actions. Log intent checks, denials and escalations for later review. Authenticate the acting agent before evaluating its permitted runtime scope. | ||
| NIST Zero Trust (SP 800-207) | Continuous Verification | The question argues for checking authority at the moment of action, not only earlier. |
| Recommendation — Verify each request at execution time instead of trusting prior approval alone. | ||
Practitioner Guidance
What to prioritise: Put runtime intent checks on the actions that create irreversible or externally visible effects first, then expand outward. If the agent can write, send, spend, delete, deploy or exfiltrate, those actions deserve execution-time policy before any broader review workflow.
Decision rule: If a human cannot realistically approve the action before it becomes stale, low-value or unsafe to delay, make the machine boundary primary and reserve human review for exceptions, escalations and sampled oversight.
What to verify: Confirm that the system can evaluate intent against the current task, current principal, current target and current scope at the point of execution, and that blocked actions fail closed rather than merely logging a warning.
Practitioner takeaway: Use humans to govern ambiguity and learn from outliers, but use runtime checks to control the agent’s ordinary power path; that is what keeps the system both usable and safe.
Related resources from NHI Mgmt Group
- When should organisations prioritise human review over fully autonomous AI decisions?
- When should organisations prioritise runtime guardrails over model-focused AI controls?
- When should organisations prioritise runtime AI controls over static approvals?
- When should organisations prioritise runtime protection over pre-release checks?