What the term really means
An AI approach in which a system receives a goal, selects subsequent actions and evaluates results within defined boundaries.
How it works in practice
In an agentic workflow the system does not receive one isolated instruction; it plans steps, evaluates intermediate results and chooses what to do next.
The decision to make before implementation
Define how long it may operate independently, when a loop must stop and which decisions require approval.
How to verify that it works
Evaluate the complete journey rather than one response: goal completion, step count, incorrect actions and recovery time are the useful measures. Compare results with an agreed baseline and review routine cases, difficult exceptions and human hand-offs separately. A practical Agentic AI test should have an owner, a review date and a recorded example of an outcome the team will not accept.
The PAR HOUSE Agency approach
We approach Agentic AI from the workflow rather than a tool demonstration. Define how long it may operate independently, when a loop must stop and which decisions require approval. We then build a small measurable scope, record assumptions and expand only after quality review.
