AI Workflow Lab
An interactive proof-of-thinking tool for exploring the tradeoffs inside AI workflows, agent loops, eval coverage, and operator review.
This is a small product version of how I think about AI systems. The question is usually not just whether the model can do a task. It is where the workflow should stay deterministic, where the model should have room, and what kind of proof is worth shipping first.
The controls are intentionally simple. Change the surface, the amount of model freedom, the failure cost, and the context quality. The lab then shifts the architecture, the eval posture, the review burden, and the first artifact I would want to build.
Interactive Lab
AI workflow lab
A small deterministic model of the workflow questions I care about most: where the system should stay tight, where the model should have room, what needs review, and what kind of proof asset is worth shipping first.
Presets
Tune the workflow
Live architecture
Operator router
Use deterministic routing and tool scope to keep the workflow legible while the model handles the fuzzy parts.
Ship first
Operator cockpit
Build the review queue, audit trail, and recovery actions before you try to make the model look magical.
What to measure
Task completion rate inside the workflow
Human handoff rate
Latency per completed task
What breaks first
The real bottleneck is deciding what the workflow should own.
If the model stays too constrained, you end up with software theater instead of leverage.
The easiest trap is polishing the demo before you know the operator loop is worth keeping.
Good next moves
Start with the narrowest workflow slice that still proves real usefulness.
Log the system behavior from day one so you do not mistake intuition for signal.
Push clarity into the context and routing layers before adding more agent behavior.