AWS SageMaker
From templates to AI agents
Generate a starting point. Inspect every step. Keep a path back to code.
17
Pipeline step types
Each required detailed configuration and connections
- Role
- Senior Product Designer
- Team
- 12 developers and 1 product manager
- Duration
- 5 months
- Scope
- Strategy, working prototype, and production design
- 17Pipeline step types
- Each required detailed configuration and connections
- 4Agent tools prototyped
- Generation, editing, configuration, and validation
- 3 moPrototype to production design
- Including user and leadership validation
From fixed templates to flexible workflows.
Project context
- Problem
- Static templates could not cover each team's workflow.
- My decision
- I proposed an agent that composes pipelines and tested it in working code.
- Tradeoff
- Generation needed an escape hatch: inspect, edit and continue in a notebook.
- Project outcome
- Leadership approved the direction and production designs were delivered. The 50% speed improvement remained a post-launch estimate.
The starting point
Ask before acting
Make every step visible
Keep the details editable
From canvas to code
