I've built 12+ production AI agent systems across development, DevOps, and data operations. Here's why the current hype around autonomous agents is mathematically impossible and what actually works in production.
It’s a really good illustration of where the sweet spot for these tools is. Trying to have LLMs solve problems end to end is simply not practical barring some new revolutionary innovation down the road. However, these tools are getting very good at solving small and focused tasks. It’s becoming increasingly evident that these tools aren’t replacing humans, but automate a lot of tedious labour for the developer allowing you to focus mostly on high level functionality of the feature you’re working on.
What the author describes matches my experience as well. I quickly learned that the more focused you make the tasks the better the results. I also quickly learned that if the model doesn’t come up with a good solution on the first shot, it becomes increasingly unlikely that it will improve as it iterates on it. What typically happens is that it just keeps adding kludges on top of kludges instead of addressing underlying mistakes in the solution. What I’ve started doing is sketching out the scaffolding for the code where I effectively create a template with the function signatures and overall structure I want, and then let the agent fill in the blanks. My experience is that this tends to work pretty well majority of the time.
It’s a really good illustration of where the sweet spot for these tools is. Trying to have LLMs solve problems end to end is simply not practical barring some new revolutionary innovation down the road. However, these tools are getting very good at solving small and focused tasks. It’s becoming increasingly evident that these tools aren’t replacing humans, but automate a lot of tedious labour for the developer allowing you to focus mostly on high level functionality of the feature you’re working on.
What the author describes matches my experience as well. I quickly learned that the more focused you make the tasks the better the results. I also quickly learned that if the model doesn’t come up with a good solution on the first shot, it becomes increasingly unlikely that it will improve as it iterates on it. What typically happens is that it just keeps adding kludges on top of kludges instead of addressing underlying mistakes in the solution. What I’ve started doing is sketching out the scaffolding for the code where I effectively create a template with the function signatures and overall structure I want, and then let the agent fill in the blanks. My experience is that this tends to work pretty well majority of the time.