Automation is seductive.
Once AI entered the workflow, it became easy to automate almost everything:
- boilerplate generation
- refactoring
- tests
- migrations
- integrations
The promise is simple: less manual work, more leverage.
The risk is just as simple: losing control of your own system.
Over time, I’ve learned that the real challenge isn’t how to automate repetitive coding tasks.
It’s how to automate them without outsourcing thinking.
The First Mistake: Automating Before Understanding
Early on, it’s tempting to automate as soon as something feels repetitive.
That’s backwards.
Repetition is a signal. But it’s not always a solution.
Before automating anything, I force myself to answer:
- Why does this task exist?
- What decision is embedded in it?
- What assumptions does it rely on?
- What breaks if this changes?
If I can’t explain the task clearly, I don’t automate it.
Automation amplifies design. It doesn’t fix it.
I Separate “Execution” From “Judgment”
This is the most important rule I follow.
Some work is:
- mechanical
- deterministic
- low-risk
- reversible
That’s execution. It should be automated aggressively.
Other work involves:
- trade-offs
- architectural intent
- risk
- long-term consequences
That’s judgment. It should stay human.
So I design my automations to:
- handle the boring parts
- surface decisions instead of hiding them
- stop and ask for confirmation at critical boundaries
If an automation removes judgment instead of supporting it, I roll it back.
I Automate Workflows, Not Just Tasks
Automating a single task is easy.
The real leverage comes from automating flows:
- code generation → formatting → tests → review scaffolding
- refactor → validation → impact summary
- migration → checks → rollback plan
Each step is simple.
The value is in:
- consistency
- ordering
- guardrails
- visibility
This way, automation becomes predictable, not magical.
Every Automation Has an “Undo Path”
I don’t trust any automation that can’t be reversed.
So I design:
- diffs instead of direct writes
- previews instead of silent changes
- commits instead of mutations
- checkpoints instead of one-way actions
This keeps me in control even when the system moves fast.
Speed without reversibility is not productivity.
It’s risk.
I Version the Rules, Not Just the Code
Most people version code.
I also version:
- prompts
- templates
- refactoring rules
- generation policies
- automation scripts
Why?
Because behavior changes over time.
If automation suddenly produces different results, I want to know:
- what rule changed
- when it changed
- why it changed
This turns automation from a black box into a governed system.
I Use AI as a Multiplier, Not a Decider
AI is excellent at:
- expanding options
- generating variants
- exploring refactors
- spotting patterns
I use it heavily for that.
But I never let it:
- decide architecture
- choose trade-offs silently
- redefine boundaries
- merge changes without review
AI accelerates my thinking.
It does not replace it.
I Keep “Intent” Human-Readable
One quiet failure mode of automation is loss of intent.
So I make sure:
- generated code includes rationale comments
- PRs include a summary of why, not just what
- scripts explain their purpose
- rules are documented in plain language
If I can’t explain what an automation is doing in simple terms, it’s too dangerous to trust.
I Measure the Right Things
I don’t measure:
- how many lines of code were generated
- how many tasks were automated
I measure:
how often I had to undo changes
- how often automation surprised me
- how often I had to debug the automation itself
- how much thinking time I actually saved
If automation creates new classes of problems, it’s not leverage.
It’s complexity disguised as speed.
Where Automation Works Best for Me
I automate heavily in areas like:
- scaffolding
- repetitive refactors
- test generation
- formatting and consistency
- migration mechanics
- documentation drafts
And I stay very conservative around:
- core architecture
- data models
- security boundaries
- performance-critical paths
- business logic invariants
Not because AI is weak.
But because these areas encode long-term decisions.
The Real Principle I Follow
Automation should:
- reduce friction
- increase consistency
- preserve intent
- keep humans in the decision loop
- make systems calmer, not more fragile
If it doesn’t do all five, I don’t ship it.
The Real Takeaway
The goal is not to automate more.
The goal is to automate wisely.
In an AI-driven workflow, the highest leverage isn’t in how much work the system does for me.
It’s in how well the system:
- reflects my intent
- respects boundaries
- stays reversible
- and keeps me in control of the outcomes
That’s how I automate repetitive coding tasks without losing control.
Not by trusting automation more. But by designing it more carefully.
Top comments (2)
Once AI entered the workflow, it became easy to automate almost everything.
This really hits the nerve most “AI workflow” posts dodge.
What you’re describing maps almost perfectly to how I’ve learned to survive automation at scale: execution is cheap, judgment is sacred. The line you draw between the two is the whole game. The moment automation starts making decisions instead of exposing them, you’re no longer moving faster—you’re just moving blind.
I especially like the emphasis on undo paths and versioning rules, not just code. That’s the part people skip, and it’s why automations feel “haunted” six weeks later when behavior shifts and no one knows why. Treating prompts, templates, and refactor rules as first-class, versioned artifacts is exactly how you keep leverage without chaos.
The “automate workflows, not tasks” point also lands hard. Single-task automation gives dopamine. Flow automation gives calm. Ordering, guardrails, and visibility are what actually reduce cognitive load, not raw speed.
This post reads less like “how I use AI” and more like “how I keep authorship in a world that wants to take it from me,” which I think is the real conversation we should be having. Automation that preserves intent and reversibility isn’t cautious—it’s professional.