From Autocomplete to Autonomy: How Agentic Coding Changed the Way I Build Software
Agentic coding is the first time AI feels less like a tool and more like a collaborator. Here’s how it changed the way I build software.
From Autocomplete to Autonomy: How Agentic Coding Changed the Way I Build Software
For a long time, AI in programming meant one thing: autocomplete.
Helpful, sometimes impressive, but ultimately passive.
It reacted to what I typed. It didn’t understand why I was building something.
Agentic coding is the first time AI feels less like a tool — and more like a collaborator.
Not because it writes more code.
But because it understands intent.
What Agentic Coding Actually Is
Agentic coding is not about generating snippets faster.
It’s about delegating a goal, not a sequence of instructions.
An AI agent:
- receives a task, not a function signature
- plans the steps needed to achieve it
- modifies multiple files
- runs tests or builds
- reacts to errors
- iterates until the goal is met
Instead of thinking in lines of code, you think in outcomes.
“Add pagination to the orders list, make it accessible, don’t break the API, and include tests.”
That’s the input.
The rest becomes a loop: plan → execute → verify → refine.
A Practical Example: Adding Pagination
Let’s take a very normal frontend task.
Traditional approach
- Analyze the existing code
- Update the GraphQL query
- Handle cursors and page info
- Update UI components
- Add loading and empty states
- Write tests
- Fix regressions
All doable — but time-consuming and full of boilerplate.
Agentic approach
You describe the goal and constraints:
Add pagination to the orders list.
Use existing GraphQL pagination.
Keep the UI accessible.
Don’t change the public API.
Add basic tests.
The agent:
- Reads the repository
- Finds the relevant query and components
- Proposes a plan
- Implements the changes
- Runs tests
- Fixes issues it introduced
My role shifts from executor to reviewer and decision-maker.
I’m not skipping responsibility — I’m focusing it where it matters.
How This Changed My Role as a Developer
The biggest shift wasn’t speed.
It was where my attention goes.
Less time spent on:
- boilerplate
- wiring data
- repetitive patterns
More time spent on:
- architecture
- constraints
- edge cases
- naming
- reviewing diffs
- asking “does this actually make sense?”
I don’t write less code.
I write more important code.
Where Agentic Coding Fails
This part matters.
Agentic coding breaks down when:
- the architecture is unclear
- requirements are vague
- domain rules live only in someone’s head
- the codebase is inconsistent
AI agents don’t magically fix bad foundations.
They amplify whatever is already there — good or bad.
That’s why this approach rewards developers who:
- understand systems
- can define constraints clearly
- can recognize wrong solutions quickly
Why This Matters (Especially for Frontend)
Frontend work today is less about rendering buttons
and more about orchestrating complexity:
- APIs
- state
- performance
- accessibility
- product logic
Agentic coding acts as a force multiplier.
Not by replacing developers —
but by rewarding those who think in systems instead of files.
The competitive edge is no longer typing speed.
It’s clarity of thought.
Learning to Work With Agents
The core skill going forward isn’t “prompting tricks”.
It’s:
- expressing intent
- defining constraints
- reviewing critically
- knowing when not to accept a solution
The better your thinking,
the better the agent performs.
Agentic coding doesn’t lower the bar.
It raises it — and makes the work more interesting.
If you’re a developer today, learning how to work with AI agents isn’t optional.
But learning how to think clearly still is — and always will be.