Isn’t this pretty much how everyone uses agents?
Feels like it’s a lot of words to say what amounts to make the agent do the steps we know works well for building software.
Isn’t this pretty much how everyone uses agents?
Feels like it’s a lot of words to say what amounts to make the agent do the steps we know works well for building software.
I think most of these writeups are packaging familiar engineering moves into LLM-shaped language. In my experience the real value is operational: explicit tool interfaces, idempotent steps, checkpoints and durable workflows run in Temporal or Airflow, with Playwright for browser tasks and a vector DB for state so you can replay and debug failures. The tradeoff is extra latency, token cost and engineering overhead, so expect to spend most of your time on retries, schema validation and monitoring rather than on clever prompt hacks, and use function calling or JSON schemas to keep tool outputs predictable.
> I think most of these writeups are packaging familiar engineering moves into LLM-shaped language.
They are, and that's deliberate.
Something I'm finding neat about working with coding agents is that most of the techniques that get better results out of agents are techniques that work for larger teams of humans too.
If you've already got great habits around automated testing, documentation, linting, red/green TDD, code review, clean atomic commits etc - you're going to get much better results out of coding agents as well.
My devious plan here is to teach people good software engineering while tricking them into thinking the book is about AI.
G is posting this slop so Anthropic sends him his dinner invitation this month, give him a break.