> Is my suspicion wrong
Nope you are 100% correct. All of the models will code themselves into local maxima then spend more and more time chasing their tails as every change introduces new bugs, the agent adds more complexity, and the code base just spirals. Best case you end up with a bunch of parallel implementations that all work slightly differently.
It helps a lot of you pay attention to what it is doing and impose order, then regularly spend time refactoring - even if you are having claude do the work itself - you have to spend the time on the overall architecture and design.
I just experienced this with an internal productivity tool. Without claude this tool wouldn't exist - there would never be the justification to take the required time to build it. On the other hand all the interlocking refreshing rules for sourcing data from its inputs got so byzantine it was impossible to add new features or change anything without causing new regressions. Claude built a web of ad-hoc timers, refresh kick-offs, monitors, etc to solve each feature problem or fix some bug but the whole thing was impossible even for claude to understand. It took a special large workflow session to find most of the mess and even then during the refactor it kept finding new things.
I had to stop all feature work for going on four weeks and burn a ton of tokens to build a refreshing/syncing engine that could track dependencies, priorities, etc and fully test it and just now today I am finally able to get feature work going again.
On another feature an agent came up with a state object containing 55 inter-related fields with all kinds of interlocking dependencies. Something so complex no one could hope to understand it. And I find the agents aren't really much better at understanding such things than humans are. If you leave it like that the agent will just perform worse and worse on that part of the code as time goes on. It will make assumptions that force it into bad design corners, miss edge cases, and flat out miss things no matter how well you prompt. Agents benefit from encapsulation and simplicity just as much as humans.
Nope you are 100% correct. All of the models will code themselves into local maxima then spend more and more time chasing their tails as every change introduces new bugs, the agent adds more complexity, and the code base just spirals. Best case you end up with a bunch of parallel implementations that all work slightly differently.
It helps a lot of you pay attention to what it is doing and impose order, then regularly spend time refactoring - even if you are having claude do the work itself - you have to spend the time on the overall architecture and design.
I just experienced this with an internal productivity tool. Without claude this tool wouldn't exist - there would never be the justification to take the required time to build it. On the other hand all the interlocking refreshing rules for sourcing data from its inputs got so byzantine it was impossible to add new features or change anything without causing new regressions. Claude built a web of ad-hoc timers, refresh kick-offs, monitors, etc to solve each feature problem or fix some bug but the whole thing was impossible even for claude to understand. It took a special large workflow session to find most of the mess and even then during the refactor it kept finding new things.
I had to stop all feature work for going on four weeks and burn a ton of tokens to build a refreshing/syncing engine that could track dependencies, priorities, etc and fully test it and just now today I am finally able to get feature work going again.
On another feature an agent came up with a state object containing 55 inter-related fields with all kinds of interlocking dependencies. Something so complex no one could hope to understand it. And I find the agents aren't really much better at understanding such things than humans are. If you leave it like that the agent will just perform worse and worse on that part of the code as time goes on. It will make assumptions that force it into bad design corners, miss edge cases, and flat out miss things no matter how well you prompt. Agents benefit from encapsulation and simplicity just as much as humans.