🚀 Agentic Coding: The Moment I Realised the Game Has Changed

I had one of those “hang on… did that really just happen?” moments this week while fixing a P2 production issue using Warp's AI Agent Mode.

What used to take me a few hours of context switching and manual debugging was done in less than an hour, and I didn’t even open an IDE.

Six months ago, I’d have done the usual dance: copy something from ChatGPT, paste it into my IDE, fix the import errors, adjust for project conventions, run the tests manually, debug the failures, rinse and repeat.

This time, I just described the problem.
- The agent read through the codebase
- Investigated the issue
- Showed me what it found
- Proposed a fix
- And wrote the tests to prove it worked

It traced through 20+ files, created a failing test to reproduce the bug, fixed it properly (with clean error handling), added four edge-case tests, and even optimised the logic for performance.

Then it:
- Created a Jira ticket
- Generated commits with detailed messages
- Cherry-picked to a hotfix branch
- Tagged the release

All while I just guided the direction and reviewed what it was doing.

The thing that surprised me most wasn’t the speed; it was the confidence I felt as I watched it work. Not blind trust, but confidence that came from seeing its reasoning step-by-step, watching it apply proper red–green–refactor, checking that all the tests passed, and realising it even followed our git workflow.

It still feels like cheating somehow, but the best kind. The kind that makes engineering fun again. I found myself thinking more strategically instead of mechanically typing.

The skill now isn’t about writing every line yourself; it’s about asking the right questions, evaluating the reasoning, understanding the architectural implications, and knowing when to step in.

This isn’t the future of software engineering. This is already happening.
And it’s only getting better from here.