One key difference I have noticed is the upfront cost. With agentic coding, I felt a higher upfront cost: I have to think architecture, constraints, and success criteria before the model even starts generating code. I have to externalize the mental model I normally keep in my head so the AI can operate with it.
In “precision coding,” that upfront cost is minimal but only because I carry most of the complexity mentally. All the design decisions, edge cases, and contextual assumptions live in my head as I write. Tests become more of a final validation step.
What I have realized is that agentic coding shifts my cognitive load from on-demand execution to more pre-planned execution (I am behaving more like a researcher than a hacker). My role is less about 'precisely' implementing every piece of logic and more about defining the problem space clearly enough that the agent can assemble the solution reliably.
Another observation has been that since the cost of writing code is minimal as agents are delegated to write them, there is a need for me to shift and context and also take up the QA role to evaluate the agents output.
Would love to hear your thoughts?
dgunay•2mo ago
mettamage•2mo ago
So yea, fair enough.
Though, the shift that the OP describes, yea I can see that. Writing tests has become way more important. Or well, it feels more important. From a testing perspective, we should see ourselves agents too (aka bug making machines), that's why you need tests. The silly bias I always had was "but I'm writing the code! It'll be fine, I won't make bug- oh... why can't I close my modal window when I click on the x symbol?"
But yea, the apparent need for testing is definitely much more there. The need for architecting it well is also there as LLMs still seem to be a bit in tutorial land with that one. There are a few more things like that.