AI Observer / Runtime-Aware Dev Agent

Codex
1

Feature Request: AI Observer / Runtime-Aware Dev Agent

I want to add a related Codex developer workflow request: an AI Observer layer for runtime-aware development.

The current problem is that LLMs help write code, but remain mostly blind during runtime. They often cannot see browser behavior, live DOM changes, extension popup state, console logs, workers, runtime state, or visual UI failures. This forces users to repeatedly explain obvious bugs and creates unnecessary clarification loops.

The requested direction is a browser and runtime aware assistant layer that can safely observe live development environments and report precise debugging feedback.

Key capabilities would include browser vision, visual UI verification, DOM state awareness, console and worker monitoring, extension popup observation, progress and readiness tracking, safe test mode, a user notes queue, one active worker enforcement, visual error reporting, and persistent project context.

The assistant should clearly distinguish static checks, dry runs, visual runtime tests, and live browser verification. It must not hallucinate test results; it should state exactly what it actually verified and how.

Desired workflow: the user says, Fixed it, test again. The AI Observer watches the browser and runtime, tests safely, reports the exact issue, suggests a focused fix, and keeps project memory and context.

Long term, this could become an IDE and browser connected AI orchestration system that is conversational, runtime aware, visually aware, project persistent, and useful for agent assisted development workflows. This would reduce repeated clarification, make debugging faster, and improve developer trust in AI coding agents.

2

Follow-up idea: AI Desktop Shell 0.1 / Runtime Beacon

A natural extension of AI Observer would be the ability to attach the assistant to the actual place where the user is working, almost like dropping a location beacon on a map.

Instead of only chatting with an AI, the user could pin the AI to a specific browser tab, extension popup, app window, terminal, file, dev server, DOM element, or screen region. The user could point at the problem and say: work here. The assistant would immediately know the active runtime context.

For Windows, this could start as a lightweight desktop overlay rather than a full OS replacement. It would sit above the normal desktop and provide Pin current window, Point to region, Observe, Safe test, and Continue. Under the hood it could combine screenshot and OCR, accessibility tree, process and window metadata, browser DOM and console when available, and terminal or dev-server logs.

The key UX is that the AI should always know and show what it is attached to: active app, browser tab, popup, worker, process, or selected screen region. It should report status such as attached, observing, testing, blocked, needs confirmation, runtime error, or ready.

This would let a developer literally point the assistant at an app and say: fixed it, test again. The AI would not need repeated explanations; it would already have the beacon, the runtime surface, and the project context.

3

Follow-up: user feedback analysis and product simplification principle

I want to add one more important lesson from real usage: the biggest risk is not missing another advanced feature, but making the tool too complex.

The core user pain is this: separate pieces used to work, and the user only wanted a convenient launcher and a nicer interface. But when too much architecture, too many panels, too many controls, and too much logging are added, the result can become slower, harder to use, and less stable.

For this kind of runtime-aware assistant, the product principle should be: do not make it more complex; make it more convenient. Every new feature should answer one question: does this actually make work faster, simpler, or more stable? If not, it should not be added to the main UI.

The popup should stay minimal. It should be a launcher, not a developer dashboard. Workers should do the background work. AI should help with text and decisions. Browser/runtime observation should stay focused and explain what is actually being verified.

The old working mechanics should be preserved first: inject logic, reply logic, like logic, the existing reply database, speed, and separate workflows. New architecture should wrap the working mechanics carefully instead of replacing them with a bigger system.

A good minimal UI would show only the essentials: likes/hearts, saved replies, AI on/off, stop, reply count, speed, and one-line status. Debug panels, giant logs, worker controls, readiness tables, and extra settings should be hidden unless explicitly needed.

The main message is simple: a runtime-aware AI tool should feel small, fast, stable, and useful. It should not become a giant AI control center.

4

Follow-up: Ambient AI companion / cursor drop

Another useful direction would be to make the assistant feel less like a big app and more like a small always-available companion, closer to a smart home speaker or the old Windows paperclip idea, but modern and practical.

The user should not always have to open a large AI center. There could be a tiny floating drop near the mouse cursor or screen edge. It stays out of the way, but is always available. The user can click it, drag it to an app, pin it to a window, or ask it what is happening here.

This should still follow the simplification principle: small, fast, optional, and non-intrusive. If the user turns it on, it is there. If not, it should stay quiet. It should not become another dashboard.

Possible behavior: the drop can show one-line status, listen for a short command, attach to the current window, open quick actions, or enter safe test mode. It could also support themes or skins, but those should be cosmetic and never slow down the core tool.

The key idea: the assistant should feel like a helpful presence under the user’s hand, not a heavy control panel. Always nearby, but never in the way.

5

Follow-up: Lens mode / screenshot-to-action

A related interaction pattern is a visual lens mode, somewhat like Big Picture overlays or screenshot selection tools, but connected to the runtime assistant.

The user presses a lens button, selects an area of the screen, and the AI uses vision to understand what that area contains. It should then map the selected visual region back to the real target when possible: a DOM node, a browser button, an app control, a popup element, a terminal line, or a process/window.

This is important because users often do not want to explain the UI verbally. They want to point at the broken place. The assistant should be able to say: I see the disabled button here, I found the matching DOM element, I see the console error connected to it, and I can test or suggest the focused fix.

The key is not only taking screenshots. The vision has to be actionable. A screenshot crop should become an anchor into the live runtime: screen region to UI element, UI element to logs/state, logs/state to next action.

This would make the assistant feel much more practical: click lens, highlight the problem, and the AI finds the place, waits for the button/state if needed, and continues from there.

6

Follow-up: direct user feedback about listening and simplicity

I want to add direct UX feedback from this same long project session.

The biggest failure is not raw coding ability. The biggest failure is listening, restraint, and protecting the working baseline.

The user had a manual workflow and a human-made etalon that worked. The assistant did help with some real wins: automated button clicking and a cleaner visual launcher. But too often it drifted away from the etalon, made second and third versions, added complexity, watched the wrong signals, spent tokens on theory, and did not stop fast enough when told to stop.

From the user side, this feels like working with a schoolkid who keeps doing extra work instead of learning the actual instruction. A simple task that worked by hand becomes fragile because the AI wants to redesign it.

The requested behavior is simple: protect the etalon, keep the popup as a launcher, keep workers doing the work, verify the live browser/runtime before claiming success, and answer briefly. Simplicity should be treated as a hard product requirement, not a style preference.

There is still a valuable idea here: an AI helper that talks, observes, and helps simplify future ideas with saved context for each job. It should remember the exact working baseline and ask: do you mean this same etalon? It should watch the current browser, popup, console, worker state, and DOM, then give focused feedback without inventing a new architecture.

The assistant should not try to be clever first. It should listen first, preserve what works, and only simplify or automate the next step.

7

Follow-up: Codex credit usage and workspace safety

I am a paying user and I tried to use Codex as a local coding assistant to continue work on my Windows software project. The core problem is that my paid usage was consumed not by productive work, but by repeatedly correcting the agent’s own workflow mistakes.

Main issue:
Codex spent a large part of my limited credits on fixing its own wrong assumptions, wrong workspace usage, and unnecessary detours. It worked in or referenced places like OneDrive/Desktop instead of keeping all work inside a safe sandbox or project working copy. I had to keep redirecting it, explaining not to touch my real working folders, not to use Desktop/OneDrive as test space, and to keep logs, backups, test output, and temporary files isolated.

This feels unfair because credits are charged for agent confusion and self-correction, not only for completed useful work.

Additional quota UX issue:
When the user runs out of Codex messages, the current warning banner feels disruptive and punishing. It blocks the workflow with a message like “you have run out of Codex messages” and pushes the user toward refreshing or adding credits, but it does not give enough live context while the work is happening.

A better experience would be a clear live usage timer/counter: how many messages or credits were spent, how much remains, when the limit resets, and roughly how expensive a long scan/refactor/test run may be before starting it. The warning should be compact and predictable, not a surprise banner that interrupts the coding flow after the user has already invested money and attention into the session.

A better quota model would be smaller but more frequent resets, for example twice per day, instead of leaving the user locked out for many days after one bad agent session. This would make experimentation less punishing and would help users continue real work even when an agent run goes wrong.

What went wrong:

  1. The agent did not clearly separate original project, working copy, sandbox, temporary files, logs, reports, and backups.
  2. It did not strongly prevent Desktop, OneDrive, Downloads, root folders, or personal folders from being used as accidental workspaces.
  3. It required repeated user corrections just to follow basic safety rules.
  4. Credits were consumed while I was steering the agent back to the correct workflow.
  5. The user is left paying for agent mistakes instead of actual completed project progress.
  6. There is not enough visibility into what actions cost credits and why.
  7. There is no clear safe project mode for non-programmers who want the agent to work only inside one chosen folder.

Suggested improvements:

  1. Add a mandatory Project Sandbox Mode: the user selects one project folder, the agent creates a working copy, all edits/logs/reports/tests/backups stay inside the sandbox, and Desktop/OneDrive/Downloads/system/personal folders are blocked by default.

  2. Add a read-only audit first mode: no file changes, no system-modifying commands, only inventory, project map, risk list, and cleanup plan.

  3. Add credit protection: do not charge full credits for agent self-correction after its own mistake, show estimated credit cost before long scans or refactors, pause before large context/file operations, and warn when a task may consume most of the remaining quota.

  4. Add clearer workspace UI: current working directory always visible, modified files list always visible, files created outside the project flagged immediately, and OneDrive/Desktop usage triggers a warning.

  5. Add non-programmer safety presets: Safe audit, Clean project structure, UI-only changes, No system changes, No deletion without confirmation, No Desktop/OneDrive writes except final report.

  6. Add a way to distinguish useful task progress from setup/agent correction: completed task steps, wasted/repeated steps, files touched, commands run, and credits used per phase.

  7. Add a compact quota/credit meter directly in the Codex UI: remaining messages or credits, current session usage, reset countdown, and a warning before high-cost operations. This should help the user plan instead of being surprised by a lockout.

I understand that coding agents use compute, but the current experience feels like I am paying for the agent to learn basic safe workspace behavior on my project. A paid user should not lose most of the quota because the agent needs to be repeatedly redirected away from OneDrive/Desktop and into a sandbox.

Please make Codex safer, more transparent, and fairer for users who are not professional developers but are trying to build real local software.