Enterprise AI initiatives often stall between a promising demonstration and measurable operational impact. The missing ingredient is not a better model; it is workflow redesign.

This session presents a practical framework for selecting agentic use cases, assigning appropriate levels of autonomy, building controls into workflows, and measuring business outcomes. Attendees will leave with an Agentic Workflow Test, an Autonomy Ladder, and a 90-day playbook for moving from AI experimentation to dependable production workflows.

This session is designed for a mixed audience of engineering, product, architecture, transformation, and business leaders exploring how to move AI agents from experimentation into dependable production workflows. No specialist knowledge of machine learning or a particular agent framework is required.

The preferred format is a 35-minute presentation followed by 10 minutes of Q&A, although the material can be adapted to a 25-minute conference session or a 60-minute interactive workshop.

The session is technology- and vendor-neutral. It does not require live internet access or a product demonstration. Standard presentation facilities are sufficient: projector or large display, HDMI/USB-C connectivity, presentation clicker, and a headset microphone. Audience polling is optional but will be added to the session to gauge feedback.

This is intended to be the first public delivery of the session. The material is original and can be adapted to the conference’s industry, audience, and responsible-AI requirements. Any organizational examples will be anonymized where necessary.

The topic is particularly suitable for conferences focused on enterprise AI.

Attendees will receive practical, reusable frameworks including the Agentic Workflow Test, the Autonomy Ladder, and a 90-day implementation playbook. The session is not a sponsored presentation and will not promote a particular model, platform, or vendor.