It seems using AI agents in a software engineering workflow s not going anywhere anytime soon.
Current opinions and questions
- Where do we end up after not ‘coding’ for a while, do you lose the edge to do the thing yourself? And does that matter for the end result?
- We probably still want to keep the cost of change low.
- Software engineering skills and knowledge still matter.
- TDD is more important than ever.
- Architecture and design is more important than ever.
- How to
- How can we objectively measure if we ‘get more done’?
- Or is it just a subjective experience?
- I don’t find it enjoyable.
- It’s probably good to be very specific on your tests and specifications when generating code.
Gathering research
- Look up that one paper where experienced developers / repository owners think they were faster, but were actually 19% slower.
- Is that because models weren’t as good some months ago? (2026-03-19)
Pitfalls because I’m human
And need/want to be in the loop
- When there’s a lot of changes
- I experience the tendency of not reviewing all lines but checking a pattern that was applied.
- When the AI is busy doing things
- I can’t really do anything else, the context switches still hurt.
- It’s easy to be too generic and have it do a lot.
- It seems I tend to stick to “agentic” engineering once I started with it, because
- There’s a sunk cost fallacy.
- Changing all the stuff generated by hand is very tedious.
- I’m less intimately familiar with the code.
Lessons learned
- It’s better to be very specific about a single test for example and describe exactly the scenario you want tested.
- Review, then commit.
- Further TDD practices.