I'm convinced now that the key to getting useful results out of coding agents (Claude Code, Codex CLI etc) is having good mechanisms in place to help those agents exercise and validate the code they are writing.
At the most basic level this means making sure they can run commands to execute the code - easiest with languages like Python, with HTML+JavaScript you need to remind them that Playwright exists and they should use it.
The next step up from that is a good automated test suite.
Then we get into quality of code/life improvement tools - automatic code formatters, linters, fuzzing tools etc.
Debuggers are good too. These tend to be less coding-agent friendly due to them often having directly interactive interfaces, but agents can increasingly use them - and there are other options that are a better fit as well.
I'd put formal verification tools like the ones mentioned by Martin on this spectrum too. They're potentially a fantastic unlock for agents - they're effectively just niche programming languages, and models are really good at even niche languages these days.
If you're not finding any value in coding agents but you've also not invested in execution and automated testing environment features, that's probably why.
> At the most basic level this means making sure they can run commands to execute the code - easiest with languages like Python, with HTML+JavaScript you need to remind them that Playwright exists and they should use it.
So I've been exploring the idea of going all-in on this "basic level" of validation. I'm assembling systems out of really small "services" (written in Go) that Claude Code can immediately run and interact with using curl, jq, etc. Plus when building a particular service I already have all of the downstream services (the dependencies) built and running so a lot of dependency management and integration challenges disappear. Only trying this out at a small scale as yet, but it's fascinating how the LLMs can potentially invert a lot of the economics that inform the current conventional wisdom.
My intuition is that LLMs will for many use cases lead us away from things like formal verification and even comprehensive test suites. The cost of those activities is justified by the larger cost of fixing things in production; I suspect that we will eventually start using LLMs to drive down the cost of production fixes, to the point where a lot of those upstream investments stop making sense.
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At the most basic level this means making sure they can run commands to execute the code - easiest with languages like Python, with HTML+JavaScript you need to remind them that Playwright exists and they should use it.
The next step up from that is a good automated test suite.
Then we get into quality of code/life improvement tools - automatic code formatters, linters, fuzzing tools etc.
Debuggers are good too. These tend to be less coding-agent friendly due to them often having directly interactive interfaces, but agents can increasingly use them - and there are other options that are a better fit as well.
I'd put formal verification tools like the ones mentioned by Martin on this spectrum too. They're potentially a fantastic unlock for agents - they're effectively just niche programming languages, and models are really good at even niche languages these days.
If you're not finding any value in coding agents but you've also not invested in execution and automated testing environment features, that's probably why.
So I've been exploring the idea of going all-in on this "basic level" of validation. I'm assembling systems out of really small "services" (written in Go) that Claude Code can immediately run and interact with using curl, jq, etc. Plus when building a particular service I already have all of the downstream services (the dependencies) built and running so a lot of dependency management and integration challenges disappear. Only trying this out at a small scale as yet, but it's fascinating how the LLMs can potentially invert a lot of the economics that inform the current conventional wisdom.
(Shameless plug: I write about this here: https://twilightworld.ai/thoughts/atomic-programming/)
My intuition is that LLMs will for many use cases lead us away from things like formal verification and even comprehensive test suites. The cost of those activities is justified by the larger cost of fixing things in production; I suspect that we will eventually start using LLMs to drive down the cost of production fixes, to the point where a lot of those upstream investments stop making sense.