Spot on.
Agentic coding highlights letting the model directly code on your codebase. I guess its the next level forward.
I keep seeing agentic engineering more even in job postings, so I think this will be the terminology used to describe someone building software whilst letting an AI model output the code. Its not to be confused with vibe coding which is possible with coding agents.
In other words, “Agentic engineering” feels like the response of engineers who use AI to write code, but want to maintain the skill distinction to the pure “vibe coders.”
I entirely agree that engineering practices still matter. It has been fascinating to watch how so many of the techniques associated with high-quality software engineering - automated tests and linting and clear documentation and CI and CD and cleanly factored code and so on - turn out to help coding agents produce better results as well.
Software engineering is the application of an empirical, scientific approach to finding efficient, economic solutions to practical problems in software.
As for the practitioner, he said that they: …must become experts at learning and experts at managing complexity
For the learning part, that means Iteration
Feedback
Incrementalism
Experimentation
Empiricism
For the complexity part, that means Modularity
Cohesion
Separation of Concerns
Abstraction
Loose Coupling
Anyone that advocates for agentic engineering has been very silent about the above points. Even for the very first definition, it seems that we’re no longer seeking to solve practical problems, nor proposing economical solutions for them.Not saying that AI doesn't have a place, and that models aren't getting better, but there is a seriously delusional state in this industry right now..
From Kai Lentit’s most recent video: https://youtu.be/xE9W9Ghe4Jk?t=260
Claude gave a spot on description a few months back,
The honest framing would be: “We finally have a reasoning module flexible enough to make the old agent architectures practical for general-purpose tasks.” But that doesn’t generate VC funding or Twitter engagement, so instead we get breathless announcements about “agentic AI” as if the concept just landed from space.
Where it breaks down is any task where you discover the requirements during implementation. Most hard engineering problems are like this -- you start building, realize the data model is wrong, reshape the abstraction, and iterate. An agent can execute your architecture, but it can't tell you your architecture is the wrong one. That judgment still requires someone who understands the domain deeply enough to notice when the code is solving the wrong problem correctly.
The name matters less than recognizing this boundary. Call it agentic engineering or agentic coding, the skill is knowing which tasks to hand to the agent and which to think through yourself first.
CuriouslyC•57m ago