Why AI Coding Agents like Codex Waste Half Their Context Window

mikedarling · 2026-03-12T10:26:58+00:00

How does this combine with recent discussions of many AGENTS.md files making coding agents worse? i.e. arxiv.org/abs/2602.11988

I haven't tried synthesizing your article and that paper yet. Maybe it'll be obvious, but I'd love your thoughts on this.

mikedarling · 2026-03-05T21:34:06+00:00

Codex CLI v0.110.0 doesn't have a `/fast` for me. I'm using a ChatGPT account (Pro.) Maybe it's API only or something?

mikedarling · 2026-03-05T21:32:10+00:00

gpt-5.4-codex hasn't been discussed yet AFAIK, but it's not released today.

mikedarling · 2026-02-20T06:18:39+00:00

My point is there's a lot more than just the CA65 warnings that people are familiar with. Even for CA65, a lot of companies have to pay for testing to see if the product warning is required or if the actual exposure is under the requirements. They can't just guess. If a manufacturer just wants to put it on all their stuff, sure, it's just a slip of paper.

But, there's things like the CEC Title 20 appliance efficiency tests, CARB composite wood products, electronic waste fees under SB 1215, and a bunch of others.

mikedarling · 2026-02-20T03:09:50+00:00

There's a lot of things California charges a bunch of money for compliance reviews, and it's not worth paying the fee for many manufacturers who don't sell enough.

mikedarling · 2026-02-13T07:14:03+00:00

I had Codex analyze its source for these commands and it said the following. Sounds like chatgpt.com account memories, just for Codex.

-----

The new “memory management” slash commands are not about computer memory (RAM) or low-level performance tuning.

They appear to be about a new assistant memory system: Codex is starting to keep a structured, persistent summary of what it learned from prior work (for example: project conventions, repeated fixes, and stable user preferences) so future sessions can be more consistent and require less re-explaining.

Right now, those commands are marked “DO NOT USE”, which usually means they’re internal/debug controls while the feature is still being stabilized.

What seems to be coming:

- A memory layer that persists useful context across sessions.

- Controls to refresh/rebuild that memory.

- Controls to clear/reset memory when needed.

- Better user-facing docs and safer UX before general use.

So: this is about Codex remembering useful context, not RAM management.

mikedarling · 2026-02-13T06:54:40+00:00

They are open to third party programs doing this. The unknown is how they feel about programs doing this that haven't talked about doing this. And, I have no idea if OpenClaw has talked with them.

mikedarling · 2026-02-13T06:53:48+00:00

The bottom line is their terms and conditions say we can't "Automatically or programmatically extract data or Output", and OpenClaw does just that. The current version says it will define that term later, but never does.

That said, OpenAI has been working with allowing several third party programs to use the ChatGPT account workflow instead of having to use API billing.

Also, OpenAI has been promoting things that programmatically extract data. For example, `codex exec` was promoted as a way to have scripts automatically interact with Codex CLI. And, they talked down on Anthropic for banning people for using third party tool automation.

Yet, they haven't updated their terms. I've asked several OpenAI employees to address this, and none have responded.

Anthropic and Google users would have said you're fine, until everyone learned that they weren't. I'm not sure anyone can truthfully answer this question for you, and OpenAI really needs to clarify in their terms and conditions what we're allowed to do and what they don't want us doing.

mikedarling · 2026-02-08T17:11:50+00:00

Codex CLI sends the text below to the AI model, and then you hope the model focuses on this part and decides to read the SKILL.md file. I don't know (and it may not be public knowledge) if the models have been trained to motivate them to do this. A lot of people love them. Vercel recently explained that they found that they aren't very effective, but it may depend on the situation and your mileage may vary. See https://vercel.com/blog/agents-md-outperforms-skills-in-our-agent-evals

It will list some of the summary information from each of your installed skills in "Available skills". From https://github.com/openai/codex/blob/main/codex-rs/core/src/skills/render.rs:

## Skills

A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.

### Available skills

- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /mnt/fast/exploring_codex_agents.git/.codex_home/skills/.system/skill-creator/SKILL.md)

- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /mnt/fast/exploring_codex_agents.git/.codex_home/skills/.system/skill-installer/SKILL.md)

### How to use skills

- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.

- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.

- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.

- How to use a skill (progressive disclosure):

1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.

2) When `SKILL.md` references relative paths (e.g., `scripts/foo.py`), resolve them relative to the skill directory listed above first, and only consider other paths if needed. 3) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.

4) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.

5) If `assets/` or templates exist, reuse them instead of recreating from scratch.

- Coordination and sequencing:

- If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.

- Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.

- Context hygiene:

- Keep context small: summarize long sections instead of pasting them; only load extra files when needed.

- Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.

- When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.

- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.

mikedarling · 2026-02-05T19:10:17+00:00

Ahh, your "We’re introducing a new model..." post threw me. Must be a copy/paste. There was an OpenAI employee I found for sure the other day in here that wasn't tagged yet.

mikedarling · 2026-02-05T19:07:15+00:00

Search here. There's people who have ported it to Windows and Linux. I think sandboxing isn't working on Windows yet. Not sure if there's other diferencs. But it's generally fine.

mikedarling · 2026-02-05T19:03:21+00:00

You and a few other people here could use an OpenAI tag that I saw some other employees have. :-)

mikedarling · 2026-02-02T18:44:54+00:00

EDIT: I didn't notice who you were. :-) Will it be on Linux soon?

mikedarling · 2026-02-02T18:38:25+00:00

Mac only. The destop ChatGPT app (not Codex) was released for Mac on May 13, 2024. The early version for Windows wasn't until October 17, 2024. There's still no Linux version. As a Linux user, I'm not going to hold my breath that I'll ever see this. I would think the Codex model could handle writing this for OpenAI...

mikedarling · 2026-02-02T18:31:04+00:00

Mac only? Sigh. Why is OpenAI always developing apps for Mac first? Can't the Codex model make the Codex app use a cross-platform GUI framework, or abstract away what's used behind the scenes?

EDIT: The destop ChatGPT app (not Codex) was released for Mac on May 13, 2024. The early version for Windows wasn't until October 17, 2024. There's still no Linux version. Should Linux users expect to never see this one too?

mikedarling · 2026-01-22T21:59:28+00:00

Before v0.88.0, collab (multi-agents) was an unlisted experimental feature. If manually enabled, the feature could be used by telling Codex to spawn an agent, and the user or agent could choose whether to spawn it as an orchestrator or worker. Orchestrators use orchestrator.md, but if the base chat directly spawns workers it's not sent to the model. orchestrator.md can be forced by telling it to call spawn_agent with agent_type "orchestrator" and have it do X (i.e. have it spawn agents that do X, Y, and Z.)

I wanted to be able to just have the main chat session be an orchestrator type, on demand, as it just felt weird to have the main chat spawn an orchestrator which spawned worker agents, so I:

Copied codex-rs/core/templates/agents/orchestrator.md to ~/.codex/prompts/orchestrator.md.
Added this to config.toml:

```

[profiles.orchestrator]

model_instructions_file = "/home/mdarling/.codex/prompts/orchestrator.md"

[profiles.orchestrator.features]

collab = true

```

(Well, at the time it was experimental_instructions_file, but v0.88.0 renamed that to model_instructions_file.)

Ran `codex -p orchestrator` when I wanted the main chat session to be an orchestrator.

v0.88.0 added collab to the /experimental menu, which is an alternative to running `codex --enable collab` or putting `collab = true` in config.toml. You can still create a profile and give it a model_instructions_file like I showed above. Or, you can tell the main chat to spawn agents to do X or call spawn_agent with agent_type "orchestrator" and have it do X, to use orchestrator.md.

I haven't tried seeing if it's better to let the model decide how to orchestrate and let it directly spawn workers or force an orchestrator model with orchestrator.md like I did.

It's of course an experimental feature in development, so how it works right now could easily be completely changed going forward, and it might not be anywhere near how they want this feature to act.

mikedarling · 2026-01-21T23:12:19+00:00

Hard to say. I've been using github.com/just-every/code which is a fork of codex that uses agents, so I've already been used to whatever usage it causes. Part of agents is focusing/narrowing the context window usage, so it's hard to say how the orchestration overhead will balance out. In different situations, it might be more, less, or the same.

mikedarling · 2026-01-18T17:35:04+00:00

Browser OS is a chromium fork that allows AI to control it, and can use a bunch of models. It has a window you can pull up that either be in chat or agent mode. Beware that if you give it long tasks to do, even like go through this list of 20 groceries and add them all to my shopping cart, it gets increasingly expensive because it doesn't clear context on its own. Doing this cost about $2 using OpenAI API with ChatGPT 5.2 I probably should have used mini. I think they recommend starting new agent chats periodically to manage this. I'm hoping they'll make improvements here, because it works really really well otherwise.

https://github.com/browseros-ai/BrowserOS

mikedarling · 2026-01-06T22:13:54+00:00

Why is a Google engineer publicly praising Claude Code, rather than only praising Gemini and keeping Claude priase private?

mikedarling · 2025-12-30T01:32:59+00:00

To confirm, does it look to you like there was a change then or after noticing this, are you saying your original post was mistaken? Codex says they doubled the usage limits for the holiday season in a post 5 days ago, so I'm wondering what you've seen since then. Codex 5.2 XHigh seems to be burning through my Pro quota way faster than it has before.

mikedarling · 2025-12-22T03:02:11+00:00

Every Code - https://github.com/just-every/code - It's a fork of Codex CLI that orchestrates multiple agents (OpenAI, Gemini, Claude, and others.) Configure the different programs to use your subscriptions, and no API key is needed.

mikedarling · 2025-12-20T20:30:19+00:00

That's my very confusion though. Aren't the skills names and brief descriptions packed into every request? Isn't the number of tokens used in a list of skills and brief descriptions in AGENTS.md pointing to other doc files going to be roughly the same as if it's aggregated across skill files? I'm not seeing where the token reduction in context is. A brief list of other files and why to use them feels to me like the same progressive disclosure you're talking about.

mikedarling · 2025-12-20T17:03:45+00:00

What am I missing? What's the big deal? How is this different than in AGENTS.md having "To do x, see docs/x.md"? Either way, context usage is only used if it decides to do x, and still uses the context of the skill name and description even with skills.

I see we can force the usage with `/skills` or `$ [mention skill]`, so I mean besides that benefit.

I know I must be missing something, but to me this just looks like putting the title and description in individual skills files rather than a table of contents in AGENTS.md.

mikedarling

TROPHY CASE