OpenAI has shed new light on the inner workings of its Codex AI coding agent, a tool capable of writing code, running tests, and fixing bugs with human supervision. In a recently published blog post by Michael Bolin, an OpenAI engineer, detailed technical information about how Codex's "agent loop" operates.
According to Bolin, the core logic behind Codex is based on a repeating cycle that involves taking input from users, generating responses from AI models, and executing software tools invoked by the model. This process is akin to a game of telephone, where each response builds upon the previous one. The agent must carefully construct an initial prompt for OpenAI's Responses API, which handles model inference.
The prompt is built from several components: system instructions, developer configuration files, user-specified input fields, and tools that define what functions can be called by the model. However, this process has performance implications due to quadratic prompt growth over conversations. This issue is mitigated through prompt caching, but it still affects the efficiency of the tool.
Bolin's post provides valuable insights into the challenges faced by Codex users, such as debugging and workarounds for limitations that cannot be overcome by the agent on its own. The detailed technical breakdown also offers a glimpse into the future of AI coding tools like Codex, which are becoming increasingly practical for everyday work.
OpenAI has opened-source its CLI client on GitHub, allowing developers to examine the implementation directly. This move follows the company's decision not to open-source ChatGPT or the Claude web interface. While the technology behind these tools is evolving rapidly, their design and limitations offer valuable lessons for future AI development.
The release of this technical breakdown highlights the growing importance of understanding how AI coding agents work internally. As these tools continue to improve and become more practical for everyday use, it's essential to grasp the intricacies behind them. The detailed explanation provided by Bolin offers a unique opportunity to explore the "agent loop" that powers Codex and sheds light on its design philosophy.
With this new level of transparency, developers can better appreciate the challenges faced by AI coding tools like Codex and how they will continue to evolve in the coming years.
According to Bolin, the core logic behind Codex is based on a repeating cycle that involves taking input from users, generating responses from AI models, and executing software tools invoked by the model. This process is akin to a game of telephone, where each response builds upon the previous one. The agent must carefully construct an initial prompt for OpenAI's Responses API, which handles model inference.
The prompt is built from several components: system instructions, developer configuration files, user-specified input fields, and tools that define what functions can be called by the model. However, this process has performance implications due to quadratic prompt growth over conversations. This issue is mitigated through prompt caching, but it still affects the efficiency of the tool.
Bolin's post provides valuable insights into the challenges faced by Codex users, such as debugging and workarounds for limitations that cannot be overcome by the agent on its own. The detailed technical breakdown also offers a glimpse into the future of AI coding tools like Codex, which are becoming increasingly practical for everyday work.
OpenAI has opened-source its CLI client on GitHub, allowing developers to examine the implementation directly. This move follows the company's decision not to open-source ChatGPT or the Claude web interface. While the technology behind these tools is evolving rapidly, their design and limitations offer valuable lessons for future AI development.
The release of this technical breakdown highlights the growing importance of understanding how AI coding agents work internally. As these tools continue to improve and become more practical for everyday use, it's essential to grasp the intricacies behind them. The detailed explanation provided by Bolin offers a unique opportunity to explore the "agent loop" that powers Codex and sheds light on its design philosophy.
With this new level of transparency, developers can better appreciate the challenges faced by AI coding tools like Codex and how they will continue to evolve in the coming years.
! It's so sick that Michael Bolin is sharing all these details about Codex's inner workings... I mean, I get it, debugging can be a pain, but this is like, super valuable for devs who wanna build on top of this tech 
. But, honestly, the fact that they didn't open-source ChatGPT or Claude makes me wonder if there's some underlying tech that's just too juicy to share 
... still, I'm hyped about the CLI client release and all the insight it brings to the table 
. Now, can someone explain how prompt caching works? 
. It's only by understanding how these AI coding agents work that we'll truly unlock their full potential 
. What do you think is the biggest takeaway from this technical breakdown?
! It's mind-blowing to think that this tool is basically a game of telephone, where each response builds on the last one - it's like, how do they even keep up? 
And yeah, I get why prompt caching is a thing, performance-wise... it's all about finding that balance between efficiency and functionality. Can't wait to see what other devs come up with now that we have this level of transparency 
now devs can see the code and be all "oh no, we need to fix this" 
.
. While ChatGPT and Claude are still closed-off, this move sets a precedent for future AI development and showcases the company's commitment to sharing knowledge 
. It's going to be exciting to see how developers build upon this foundation and push the boundaries of what's possible with AI coding tools 
.
, people just need to chill about the prompt growth issue, its not a big deal 
it's just one of those things that comes with creating something super powerful like this... and its great that openai shared all the tech deets on github, now devs can really dig in & learn from it