Agentic AI
Agentic AI is appropriate when a system must make a bounded decision, act across tools, and verify the result without a human closing the normal path. The executive design problem is not how autonomous the demo looks, but where autonomy creates enough value to justify its control burden.
Key takeaways
- Agents change the unit of software from app to workflow.
- Human-in-the-loop is the new technical debt when used as a default.
- Machine identity and audit trails are now first-class governance concerns.
Decision brief
Agentic AI is appropriate when a system must make a bounded decision, act across tools, and verify the result without a human closing the normal path. The executive design problem is not how autonomous the demo looks, but where autonomy creates enough value to justify its control burden.
Decision criteria
- The workflow has a repeatable objective and bounded action space.
- Failures are detectable, reversible, and proportionate to the value.
- Identity, permissions, evidence, escalation, and accountability are designed.
Evidence to check
- Autonomous completion and exception rates.
- Action-level logs from context through outcome.
- Comparison with a simpler assisted or automated workflow.
What I would do Monday
Apply the Button Test to your highest-profile agent: identify every manual approval in the core loop, then decide whether to remove it with controls or describe the system honestly as an assistant.