Move from a lab to a small pilot
Map the workflow into visual tools and prepare a controlled release. This module has 3 lessons and takes about 1h 50m. Each lesson has a practice exercise that runs real Python in your browser and a short quiz.
Start module 19 in the Learning LabMap logic to visual workflow nodes
Map each named step to its visual node role using the example mapping, preserving order and returning review for unknown names.
Visual automation builders connect nodes that receive, transform, branch, and act on data. The same contracts used in Python apply between those nodes.
Map trigger, validate, decide, and draft to event, filter, branch, and action. Unknown step names return review. This is a planning map, not an importable n8n or Make workflow.
An optional visual implementation must preserve the error route, approval gate, and replay rules. Follow the current documentation for the tool you choose.
See it in action
def solve(steps):
mapping = {"trigger":"event", "validate":"filter", "decide":"branch", "draft":"action"}
return [mapping.get(step, "review") for step in steps]
print(solve(["trigger","validate","decide","draft"]))
An optional visual implementation must preserve the error route, approval gate, and replay rules. Follow the current documentation for the tool you choose.
Choose a release mode
Return shadow if tests do not pass, pilot if tests pass with an owner and rollback ready, and hold otherwise.
A staged release starts with observation. In shadow mode, the new workflow proposes results while the existing process remains responsible for real actions.
The teaching policy stays in shadow when tests fail. It allows a pilot only when tests pass, an owner is assigned, and rollback is ready. Otherwise it holds.
A pilot should have a limited audience, a time window, monitoring, and a clear stop condition.
See it in action
def solve(tests_pass, owner_ready, rollback_ready):
if tests_pass is not True:
return "shadow"
return "pilot" if owner_ready is True and rollback_ready is True else "hold"
print(solve(False, True, True))
A pilot should have a limited audience, a time window, monitoring, and a clear stop condition.
Practical: Review a pilot readiness record
Return missing or incomplete readiness checks in this order: tests, owner, budget, rollback, approval. Only boolean True counts as complete.
A readiness record makes open work visible. This fictional pilot requires tested behaviour, an owner, a budget, a rollback plan, and an approval gate.
Return the names of required checks that are not exactly True, in a fixed order. An empty result means the checklist is complete, not that the system is guaranteed safe.
Review the evidence behind each checkbox. A checklist cannot replace real tests, permission checks, or operational ownership.
Your experiment plan
Use the browser editor or the notebook. All records and AI responses are fictional. No network calls, credentials, or real messages are used.
- Map your Python workflow to a visual tool on paper or in a tool you already have.
- Run the example and predict its result before changing it.
- Complete the function in Practice and run the checks. Add your own normal, empty, and failure cases.
- Walk through a failure and explain how the pilot would pause and roll back.
- Save the notebook with your observations and explain one limitation of the simulation.
What to hand in
- A node map and a pilot plan with evidence for every readiness item.
- A test table with inputs, expected outputs, actual outputs, and one revision.
Reflect on your results
What changes before expanding the pilot?
The automatic exercise checks the core code behaviour. Review observations and written deliverables yourself or with a mentor.
See it in action
def solve(checks):
return [key for key in ("tests","owner","budget","rollback","approval") if checks.get(key) is not True]
print(solve({"tests":True,"owner":True,"budget":True,"rollback":True,"approval":True}))
Review the evidence behind each checkbox. A checklist cannot replace real tests, permission checks, or operational ownership.
Free, open and run by a nonprofit.
This page is part of AI Automation, a free course of 20 modules and 60 lessons in the engineering.fit( ) Learning Lab. The Learning Lab is built and run by engineering.fit( ), a nonprofit founded by Abdullateef Opeyemi Bakare that helps African engineering students gain research experience. You do not need an account or pay a fee to learn.