Stanley's AI Systems Lab
AI Automation
This track focuses on reliable operating loops: the work that repeats, has clear inputs, and gets better when the boring parts are automated.
Search intent
Readers searching for AI automation tutorials usually want workflows they can implement without overbuilding a general agent.
Who this is for
Operators, founders, and product engineers automating support, research, reporting, CRM, or content operations.
Outcome
Design automations that run on clear triggers, validate inputs, escalate exceptions, and log enough context for review.
What you will build
Curriculum
Choose automation before agency
Identify when fixed steps are enough and where AI should only handle classification, summarization, or drafting.
Map the trigger and happy path
Write down inputs, validations, outputs, retries, and exceptions before picking tools.
Add human review at the boundary
Route irreversible, expensive, public, or customer-facing actions to approval.
Proof payloads to create as you learn
The goal is not to read more AI takes. Build reusable artifacts that make the workflow inspectable, reviewable, and credible.
- Automation spec with trigger, input schema, steps, exceptions, and owner.
- Exception queue table for failed runs and repeated corrections.
- Before and after workflow map showing removed handoffs.
Get the next AI Automation tutorial
Practical, proof-backed AI systems notes for founders and product engineers. No prompt-hack filler.