The core observation here is the one most agent builders miss: the model is a component, the agent is the system. Most engineering goes into the system, not the model. I've spent months building exactly that - CLAUDE.md files, memory layers, tool permission hierarchies - and the ratio feels about right. Maybe 20% of the work is model selection.
The other 80% is orchestration, context management, and deciding when to loop versus escalate. The AGENTS.md approach for project-level context is essentially what I've been doing with layered markdown files.
Different name, same principle. The bidirectional mid-task approval mechanism is the part I'd push on - in practice, most agents either run fully autonomous or fully supervised. The middle ground is harder to design than it looks.
What strikes me most about Codex's architecture is the deliberate choice to separate task execution from user interaction. Most AI coding tools try to be synchronous — you prompt, you wait, you get output. Codex flips this by running tasks in isolated sandboxes asynchronously.
The practical implication is huge: you stop thinking in "prompts" and start thinking in "tasks with acceptance criteria." That mental shift changes how you work with it entirely.
The part I'd push on: the sandboxed environment is both the strength and the ceiling. It works beautifully for well-scoped tasks, but the moment you need something that requires real-world state (auth flows, live APIs, production data) the isolation becomes a constraint. Curious how you see that tradeoff evolving as these tools mature.
Is the color coding in the App Server Process Flow diagram correct? Grey is referred as the Client color but never used. Or, I'm an idiot and missed something.
Two ByteByteGo posts in a row where the punchline is the exact same: the model is the least interesting part.
For Stripe's Minions and OpenAI's Codex both teams found that the real engineering is everything around the model.
Context management, sandboxing, prompt caching, multi-surface protocols. We have already entered the era where AI product differentiation comes from orchestration not intelligence.
The core observation here is the one most agent builders miss: the model is a component, the agent is the system. Most engineering goes into the system, not the model. I've spent months building exactly that - CLAUDE.md files, memory layers, tool permission hierarchies - and the ratio feels about right. Maybe 20% of the work is model selection.
The other 80% is orchestration, context management, and deciding when to loop versus escalate. The AGENTS.md approach for project-level context is essentially what I've been doing with layered markdown files.
Different name, same principle. The bidirectional mid-task approval mechanism is the part I'd push on - in practice, most agents either run fully autonomous or fully supervised. The middle ground is harder to design than it looks.
What strikes me most about Codex's architecture is the deliberate choice to separate task execution from user interaction. Most AI coding tools try to be synchronous — you prompt, you wait, you get output. Codex flips this by running tasks in isolated sandboxes asynchronously.
The practical implication is huge: you stop thinking in "prompts" and start thinking in "tasks with acceptance criteria." That mental shift changes how you work with it entirely.
The part I'd push on: the sandboxed environment is both the strength and the ceiling. It works beautifully for well-scoped tasks, but the moment you need something that requires real-world state (auth flows, live APIs, production data) the isolation becomes a constraint. Curious how you see that tradeoff evolving as these tools mature.
Is the color coding in the App Server Process Flow diagram correct? Grey is referred as the Client color but never used. Or, I'm an idiot and missed something.
Two ByteByteGo posts in a row where the punchline is the exact same: the model is the least interesting part.
For Stripe's Minions and OpenAI's Codex both teams found that the real engineering is everything around the model.
Context management, sandboxing, prompt caching, multi-surface protocols. We have already entered the era where AI product differentiation comes from orchestration not intelligence.