It's also helping the engineers that do have standards. A lot of what I put in my guard rails (crafted to get better outcomes for my prompts) is not exactly rocket science. Those guard rails just impose some sane engineering processes and stuff I care about.

As models get better, they seem to be biased to doing most of these things without needing to be told. Also, coding tools come with built in skills and system prompts that achieve similar things.

Two years ago I was copy pasting together a working python fast API server for a client from ChatGPT. This was pre-agentic tooling. It could sort of do small systems and work on a handful of files. I'm not a regular python user (most of my experience is kotlin based) but I understand how to structure a simple server product. Simple CRUD stuff. All we're talking here was some APIs, a DB, and a few other things. I made it use async IO and generate integration tests for all the endpoints. Took me about a day to get it to a working state. Python is simple enough that I can read it and understand what it's doing. But I never used any of the frameworks it picked.

That's 2 years ago. I could probably condense that in a simple prompt and achieve the same result in 15 minutes or so. And there would be no need for me to read any of that code. I would be able to do it in Rust, Go, Zig, or whatever as well. What used to be a few days of work gets condensed into a few minutes of prompt time. And that's excluding all the BS scrum meetings we'd have to have about this that and the other thing. The bloody meetings take longer than generating the code.

A few weeks ago I did a similar effort around banging together a Go server for processing location data. I've been working against a pretty detailed specification with a pretty large API surface and I wanted an OSS version of that. I have almost no experience with Go. I'd be fairly useless doing a detailed code review on a Go code base. So, how can I know the thing works? Very simple, I spent most of my time prompting for tests for edge cases, benchmarking, and iterating on internal architecture to improve the benchmark. The initial version worked alright but had very underwhelming performance. Once I got it doing things that looked right to me, I started working on that.

To fix performance, I iterated on trying to figure out what was on the critical path and why and asking it for improvements and pointed questions about workers, queues, etc. In short, I was leaning on my experience of having worked on high throughput JVM based systems. I got performance up to processing thousands of locations per second; up from tens/hundreds. This system is intended for processing high frequency UWB data. There probably is some more wiggle room there to get it up further. I'm not done yet. The benchmark I created works with real data and I added generated scripts to replay that data and play it back at an accelerated rate with lots of interpolated position data. As a stress test it works amazingly well.

This is what agentic engineering looks like. I'm not writing or reviewing code. But I still put in about a week plus of time here and I'm leaning on experience. It's not that different from how I would poke at some external component that I bought or sourced to figure out if it works as specified. At some point you stop hitting new problems and confidence levels rise to a point where you can sign off on the thing without ever having seen the code. Having managed teams, it's not that different from tasking others to do stuff. You might glance at their work but ultimately they do the work, not you.