OpenAI has shed light on the inner workings of its AI coding agent, Codex, in a recently published technical breakdown. The detailed post provides insight into how the company's "agentic loop" operates, offering developers a deeper understanding of the tools used to write code, run tests, and fix bugs with human supervision.
Codex is one of several AI agents that have gained significant attention in recent times, including Claude Code with Opus 4.5 and ChatGPT with GPT-5.2. These tools have become increasingly practical for everyday coding work, but their capabilities are not without controversy among software developers. While OpenAI has touted the benefits of Codex as a coding tool to help develop its own products, hands-on experience reveals that these tools can be fast at simple tasks but also brittle beyond their training data and require human oversight for production work.
In his post, Michael Bolin, an OpenAI engineer, discusses various engineering challenges faced by the team. He highlights the inefficiency of quadratic prompt growth, performance issues caused by cache misses, and bugs discovered that required fixes. These technical details are particularly noteworthy, as they offer a glimpse into the complexities involved in developing AI coding tools.
The post reveals how Codex constructs the initial prompt sent to OpenAI's Responses API, which handles model inference. The prompt is built from several components, each with an assigned role: system, developer, user, or assistant. This framework demonstrates the importance of carefully designing prompts to ensure optimal performance and accuracy.
Bolin also discusses the limitations of Codex's design choices, such as the quadratic growth of prompts over a conversation. This issue can lead to performance implications, but prompt caching helps mitigate this problem somewhat. Additionally, the ever-growing prompt length is directly related to the context window, which limits how much text the AI model can process in a single inference call.
The technical breakdown provides valuable insight into the inner workings of Codex and highlights the complexities involved in developing AI coding tools. As OpenAI continues to refine its products, it's clear that understanding these technical details is crucial for unlocking their full potential.
Codex is one of several AI agents that have gained significant attention in recent times, including Claude Code with Opus 4.5 and ChatGPT with GPT-5.2. These tools have become increasingly practical for everyday coding work, but their capabilities are not without controversy among software developers. While OpenAI has touted the benefits of Codex as a coding tool to help develop its own products, hands-on experience reveals that these tools can be fast at simple tasks but also brittle beyond their training data and require human oversight for production work.
In his post, Michael Bolin, an OpenAI engineer, discusses various engineering challenges faced by the team. He highlights the inefficiency of quadratic prompt growth, performance issues caused by cache misses, and bugs discovered that required fixes. These technical details are particularly noteworthy, as they offer a glimpse into the complexities involved in developing AI coding tools.
The post reveals how Codex constructs the initial prompt sent to OpenAI's Responses API, which handles model inference. The prompt is built from several components, each with an assigned role: system, developer, user, or assistant. This framework demonstrates the importance of carefully designing prompts to ensure optimal performance and accuracy.
Bolin also discusses the limitations of Codex's design choices, such as the quadratic growth of prompts over a conversation. This issue can lead to performance implications, but prompt caching helps mitigate this problem somewhat. Additionally, the ever-growing prompt length is directly related to the context window, which limits how much text the AI model can process in a single inference call.
The technical breakdown provides valuable insight into the inner workings of Codex and highlights the complexities involved in developing AI coding tools. As OpenAI continues to refine its products, it's clear that understanding these technical details is crucial for unlocking their full potential.
! i mean dont get me wrong its cool that theyve figured out how to make an agentic loop but the fact that they have to use prompt caching and deal with cache misses is just... 
. and what about all those bugs they had to fix 
. i cant even imagine having to deal with that in my coding work 
. its like theyre trying to make these tools as good as human coders but im not sure thats achievable 
. That's a scary thought. And what about all the bugs and performance issues they're talking about? It sounds like they still got a lot of kinks to work out 
... or maybe we just need to hold off on using them for a bit longer until they get ironed out 
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. Like, super helpful for small tasks, but when it comes to production work, you gotta have human oversight 
. And don't even get me started on those quadratic prompt growth issues... 
anyway so codex is like super fast at simple tasks but then it breaks when it's asked to do something more complex 
. and don't even get me started on the prompt length, it's like they're trying to cram as much info as possible into one little box 
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, but can you believe how fast those AI agents are getting? Like, we're talking 'simple tasks' territory here... I mean, I've seen my grandma use a coding tool faster than that! 
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but at the same time, isn't it a bit sketchy that one tool can basically do all coding tasks? shouldn't we have more diversity in AI tools or is this the future of dev? 
. But hey, I guess that's what happens when you're pushing the limits of AI, right? 
. It's not always easy to figure out how these tools work, but now we know more about Codex and its limitations. Maybe one day they'll crack the code (pun intended) and make it even better 
I mean, the convenience factor is amazing - no more carrying around heavy bottles of almond milk! But what really surprised me was how well it blended with hot chocolate... anyway, back to Codex 
I love how openai is being super transparent about codex's inner workings! it's like they're sharing all the secrets of their magic 
coding machine with us developers, and i'm literally here for it 
what i find really interesting is how important prompt design is - like, think about it, the way you phrase your questions can make all the difference in getting accurate answers 
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, right? And what about all those potential bugs and performance issues that got glossed over? Shouldn't OpenAI have done more to iron out those kinks before releasing this stuff? 
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But at the same time, I'm kinda worried about them becoming too reliant on human oversight 
What do you guys think - are these new AI agents here to stay or is it just a phase? 
. And I love how OpenAI's sharing the nitty-gritty details of Codex - it shows that they're genuinely committed to making these tools better and more robust.
. anyway, i'm low-key impressed by the technical breakdown tho - it's cool to see how codex constructs those initial prompts