LogicFolds field notes
Useful thinking for teams putting AI into real operations.
Workflow design, decision controls, integrations, and the work that turns an AI demo into a dependable system.
Latest articles
The operating layer of AI.
What orchestration does in an AI workflow
The model answers one question at a time. Orchestration is the layer that turns those answers into a reliable, multi-step business process.
The integration layer most AI projects skip
A model that can draft or classify is not connected to your business until it can read and write through a defined, permissioned interface.
Decision controls set the boundary an AI workflow can't cross
Functionality, permissions, and autonomy are three separate dials. Set all three deliberately before an AI workflow is allowed to act on its own.
Where AI opportunities actually hide in your operations
Most useful AI candidates are not the flashy ones. They sit in the repetitive, evidence-heavy steps your team already complains about.
The evaluation layer that separates pilots from production
A convincing demo is not proof a workflow holds up in production. Here is what a real evaluation practice checks, and how to build one.
What an AI workflow costs after the pilot
The build is the visible expense. Evidence plumbing, review time, evaluation and ownership are what decide whether the workflow survives its first year.
Five checks before you automate a workflow with AI
A practical test for separating a valuable operational system from an expensive demo.
Human review is a workflow step, not a safety disclaimer
Where review belongs, what the operator needs to see, and how the process should recover.
What turns an AI demo into a dependable operation
The missing layer is usually orchestration, evidence, permissions, evaluation, and recovery.