01 / Notice the patternBefore automation
First, find the work that keeps asking for the same attention.
I am looking for a real workflow to document: something repetitive, easy to lose track of, and small enough to understand end to end. The first case study will begin there.
01Receive an input
02Check it by hand
03Make a judgment
04Repeat tomorrow
0101Receive an input
0202Check it by hand
0303Make a judgment
0404Repeat tomorrow
event / needs_evidence
01 Receive an input
02 Check it by hand
03 Make a judgment
04 Repeat tomorrow
0101Receive an input
0202Check it by hand
0303Make a judgment
0404Repeat tomorrow
02 / Draw the handoffsWorking method
Before choosing a tool, make the movement of information visible.
The model is one bounded step, not the whole product. Each handoff should say what enters, what can fail, who decides, and what gets recorded.
TriggerA real event starts the flow.
ValidateBad input stops before it becomes bad output.
ReasonAI is used only where uncertainty is useful.
ReviewA person keeps decisions that carry risk.
ObserveThe system leaves a trace to inspect later.
01TriggerA real event starts the flow.
02ValidateBad input stops before it becomes bad output.
03ReasonAI is used only where uncertainty is useful.
04ReviewA person keeps decisions that carry risk.
05ObserveThe system leaves a trace to inspect later.
event / needs_evidence
Trigger A real event starts the flow.
Validate Bad input stops before it becomes bad output.
Reason AI is used only where uncertainty is useful.
Review A person keeps decisions that carry risk.
Observe The system leaves a trace to inspect later.
TriggerTriggerA real event starts the flow.
ValidateValidateBad input stops before it becomes bad output.
ReasonReasonAI is used only where uncertainty is useful.
ReviewReviewA person keeps decisions that carry risk.
ObserveObserveThe system leaves a trace to inspect later.
03 / Stay for the failureWhat changes the design
The useful lesson usually arrives after the happy path ends.
The first case study will keep one failure in detail—not as a confession, but as the moment the workflow becomes more understandable.
eventvalidation.failed
signalrequired context missing
actionstop before model call
changemake clarification explicit
01eventvalidation.failed
02signalrequired context missing
03actionstop before model call
04changemake clarification explicit
event / needs_evidence
event validation.failed
signal required context missing
action stop before model call
change make clarification explicit
eventeventvalidation.failed
signalsignalrequired context missing
actionactionstop before model call
changechangemake clarification explicit
04 / Name the horizonWithout skipping ahead
Data platform engineering is where I am heading, not the title I wear today.
The direction makes sense because every useful automation eventually asks harder questions about input quality, orchestration, state, lineage, and observability.
NowMap workflows and structure outputs.
ExploringValidation, retries, state, and traces.
NextContracts, orchestration, lineage, and quality.
01NowMap workflows and structure outputs.
02ExploringValidation, retries, state, and traces.
03NextContracts, orchestration, lineage, and quality.
event / needs_evidence
Now Map workflows and structure outputs.
Exploring Validation, retries, state, and traces.
Next Contracts, orchestration, lineage, and quality.
NowNowMap workflows and structure outputs.
ExploringExploringValidation, retries, state, and traces.
NextNextContracts, orchestration, lineage, and quality.
