If approval happens too early, the human may approve a general plan before the actual tool payload is known.
That does not mean every timeout must simply fail. The right behavior depends on the operational context.
What kind of decision is the human actually being asked to make?
That is the foundation of a real approval path.
For AI agents that can actually act, approval needs to be designed as an architecture pattern:
Which configuration? In which environment? Against which system? Using which tool? With which parameters? For how long is the approval valid? Can the agent retry? Can it change related settings? Can it choose another tool if the first one fails?
A model can be wrong, incomplete, overconfident, manipulated, or missing context. That is already a problem. But the risk changes when the system has agency: access to tools, credentials, APIs, workflow engines, infrastructure platforms, SaaS applications, ticketing systems, or customer communication channels.
Why Human Approval Gets Misdesigned
A failed post-approval execution should go to operations, not back to the original approver as if the same decision is still pending.
NIST AI Risk Management Framework Core
https://airc.nist.gov/airmf-resources/airmf/5-sec-core/
A data-access exception should go to the data owner.
A data-access exception should go to the data owner.
That is not governance.
“Send to manager” is not an approval architecture.
Some actions should never reach a human because they are prohibited.
A practical approval design separates planning from execution:
External References
Many systems ask the reviewer to approve something like:
OWASP Top 10 for LLM Applications, LLM06:2025 Excessive Agency
https://genai.owasp.org/llmrisk/llm06-sensitive-information-disclosure/A regulated workflow may require a compliance reviewer.
Timeout behavior is where approval design becomes operational.
The dangerous moment in an AI workflow is not always the model response.
