I talk SWE

@italkswe.bsky.social

I just like talking about software engineering 🤷‍♂️

Our current agentic workflow defines work outside-in. First a *why and what* document targeting the PM and the engineers. Then a high level *how* document describing in hexagonal terms how this charge can be expressed in our codebase targeting engineers /1

I truly hate the anthropomorphisation of LLMs. I don’t want to be in a parasocial relationship with my work tools. It’s nothing but a cynical engagement trap and the people responsible should be ashamed of them selfs

How do you classify which problems can be solved by autonomously by an agent and which require human judgement? Most (if not all) problems has a mix of both. Even a “perfect” agent would still need feedback in order to correct wrong assumptions

Hexagonal architecture is not highlighted enough as a core agentic guide. It does a lot of heavy lifting wrt separation of concerns, responsibility spilts and exposing the right seams for testing. In my team we also incorporated some ideas from functional core/imperative shell.

“Make the next change easy” is the core recursive algorithm of highly effective software engineering. The hard part is learning the difference between easy and fastest.

A key principle in my team when working with agents is *just-in-time*. As we refine plans from high level all the way down to tests we make sure to never let the agent get ahead of its skis. Define work one abstraction level at a time top-down then implement one vertical slice inside-out

My experience with agentic development: - many more much smaller sessions - maintain a common language with the agent - established architectural pattern (hexagonal) - UBIQUITOUS_LANGUAGE.md - behavior oriented tests are “strong prompting” - the LLMs are still worse than most people think

Which is better: A: “Your architecture is worse than it has to be” B: “Your architecture has room for improvement” A risks triggering defensiveness but B risks triggering nothing

Honestly, even the most powerful llms are not nearly as powerful as the hype would lead you to believe. Yes they can produce code which compiles but as soon as you try to manage a complex system over time you’ll see the lorem ipsum