Field notes
Practical notes on AI automation work
We write about process mapping, agent controls, implementation decisions, and the parts of automation projects that short product demos leave out.
Why we write
Good automation decisions start before a tool is chosen
A workflow can fail even when every API call works. The process may have no clear owner. The input may arrive in five formats. A model may be asked to make a decision that the team has never defined. Or the system may save two minutes while creating a new queue of exceptions.
These articles explain how we think through that work. We use concrete examples, operating numbers, and plain diagrams. The goal is to help you decide what should be automated, what should stay with a person, and what evidence a build needs before it expands.
Latest article
Start with the work behind the software
Useful next steps
Move from reading to a specific process
If you want patterns you can compare with your own operation, review six AI workflow examples. Each one shows the trigger, machine steps, human checkpoint, and final system update.
If the process needs judgement across variable inputs, read our AI agent workflow guide. If you already have a slow queue in mind, send it through the contact form. We can tell you what we would measure first.
What we cover
The operating questions behind a reliable build
We cover process selection, opportunity scoring, data quality, model evaluation, approval design, exception queues, permissions, monitoring, and maintenance. We also break down common workflows so the technical parts stay tied to the job a team is trying to complete.
Expect numbers where they help. That may be runs per month, minutes handled, review rates, error thresholds, or cost per completed job. A claim about saved time is useful only when the baseline and the new result can both be checked.
We also write about limits. Some work should use fixed rules. Some should stay manual. Some needs cleaner data or clearer ownership before automation can help. Choosing against a build can be as valuable as choosing the right one.
When a tool matters, we explain the decision it supports and the constraint it introduces. Product names change quickly. The durable questions are about access, data movement, failure handling, review, cost, and who remains responsible when the system acts.