[한글자막] 새로운 AI 코딩 패러다임: OpenAI Symphony

Hinzugefügt: 2026-05-09

[00:00:00]So, OpenAI just released this open-source repo called Symphony. On the surface level, it looks like a orchestrator that allow you to manage coding agents through ticket tracker like Linear. But, it is a lot more than just connecting Linear. It's a totally different way of interacting with agents. So, the way we use coding agent has shifted a lot for the past few months. From initially just the auto complete to primarily interactive session with coding agent to now most of us around two or three different sessions in parallel, each working isolated work tree for different features or bug fixing. And there are new tooling like Super set or conductor that has been introduced to help you run and manage different interactive coding sessions easier. But, the problem is that even with those tools, many people, including myself, will feel this burden when we are working on more than like three different sessions cuz we just can't context switch every minute. And I personally have had multiple times sending the wrong instruction to the wrong strap. So, the ceiling of how much we can get out from this coding agent is no longer the model capability, but our own attention and cognitive load. And the reason project Symphony is so interesting is that OpenAI's engineer team had this realization that the current experience has been orienting around coding session, merge PRs. But, in reality for the past decades, software workflow are largely organized around deliverables, things like issues, tasks, tickets, milestones. Engineering leaders have been managing massive amount of tasks across thousands of workers, not by reviewing everyone's PR, but looking at the final outcomes using tools like Linear, Atlassian. And OpenAI's proposed solution is move human up a level. Instead of managing two or three interactive sessions, you manage tickets. The agent works at ticket level, report back through the ticket itself, and you stay in the loop without monitoring individual sessions. The ticket tracker becomes the machine itself. And the way Symphony makes this work is almost embarrassingly simple, but very effective. It's a background process. You run it once, point to a workflow file, which we'll talk a bit more, and then run forever. Every 30 seconds, this background process will glance through your Linear board. If it finds any ticket in to-do slots, it will set up an isolated workspace and start a agent in that workspace. And the whole system has three key components. One is the scheduler, the background process that is pulling ticket data and set up workspace, manage session life cycle, and a workflow.md file that lives inside your repo. It contains configuration of scheduler and detailed instruction for coding agent to know how to work with those ticket system. And those external system like Linear is a stateful state machine for human to interact with agent. And this whole setup is actually very flexible. You don't have to use Linear, you don't have to use Codex, you can actually customize to whatever you want. But, overall implementation concept is what's interesting. And the most interesting part is this workflow.md file. It basically break down into two parts. The top part is the YAML front matter. It configures scheduler directly, like which Linear project it is, what type of ticket it should pick up, where should agent create isolated workspace, and even programmatic hooks to run after it set up the workspace. And this is very useful, so you no longer need to rely on agent to set those things up, as well as how many agents can be run parallel and specific agent settings. And after that, bottom half is a markdown file. This is the prompt agent will receive every single turn with ticket details render in. It's a standard operating procedure for handling tickets in this repo. How should agent plan task? How should agent go validate its work? And what would be considered as done? And when should outreach for human review? And what I love about this design is that the same file just live inside your repo, so it's version controlled and can be changed through normal pull request. And the file itself contains some programmatic rule that controls scheduler and also what agent does. There's no separate config service, no admin panel, no UI at all. And the team on the codebase on this workflow. So, when you are boarding new agent capability, adding new step in the process, you just very easily change this markdown file. And the rest will just follow. And this whole system is designed very flexible. You don't have to use Codex, and you don't have to use Linear. They have one example implementation in Elixir, which is programming language. But, they have this back.md file that's detailing how this framework or system is designed.

[00:03:53]So, you can just drop this file to any coding agent and ask it to build and design a system in any programming language. There are already a lot of different community attempts. Like someone building custom TUI based on the task data. And also another person already rebuilt it to support Cloud Code as agent harness. And I'm going to show you step by step how you can set these things up. But, orchestrating agent is only part of the work. As OpenAI mentioned, this whole thing only works if your coding agent's environment is set up properly in a way that it can complete tickets end-to-end atomically, which you can call it harness engineer, but fundamentally just whether your environment or codebase has been set up in the right way so agent has everything it needs to complete task end-to-end.

[00:04:31]And typical things like is the system bootable so agent can just run a script to get everything set up without spending time to figure that part out.

[00:04:38]And does the system have a proper documentation structure for different things? And I think most people does have these two things properly set up in your cloud.md or agent.md file. But, the part I think most of team didn't set up is those self-verifying tools that allow agent to do the end-to-end test after implementing something and even submit a video recording to prove that it have tested and it's working in the ticket directly just like in their demo. But, in the docs, they didn't really mention how they were handling this part. So, I did some research across many major skills. And the best one I found is this Playwright CRI tool. So, I believe many of us are pretty familiar with Playwright MCP, which allow agent to use the browser and do the task, check the logs. But, the problem before was that Playwright with MCP setup, it took huge amount of tokens in context window even when it's not needed. But, they have released this Playwright CRI tool alongside agent skill that detailing every single comment. And the most interesting comment is this video recording CRI. So, Playwright allow agent to run commands like video start and video stop to capture browser session into a MP4 or WebM video. They even have some pretty advanced video rendering capability that you can add different chapter on the screen. Like here's one example video where it can record its own session and even add new HTML element on top of the screen to annotate the action the agent took and then upload the session into Linear so you can very easily verify if things actually work. And as far as I know, other tools like Chrome DevTools MCP or agent browser don't have this video capability out of box. So, this is one very important skill that will make your whole experience complete. And meanwhile, there are also other skills that you should add. And I just take one of the repo I have as example. We have this Playwright CRI tool that has a skill as well as a list of reference for agent to know how to like record a video and tracing the debug logs. And we also have a skill here to tell agent how to start server locally. And because ours is pretty straightforward, so it's just a skill file. But, sometimes for more complicated things, you can create predefined script as well. So, agent no longer spend cognitive power on those type of stuff. And meanwhile, I also created this Linear skill that allow agent to know how to operate Linear tickets by using Linear API as well as things like upload video evidence of the test. And we actually have more documentations about different parts of system. And in the agents.md or cloud.md file, this is where we have a proper index of different documentation systems so you can always go and find the relevant information. We also give more detailed debugging skills. For example, we use Grafana to track and store all the logging production. And we added relevant Grafana log skill in our repos so the agent can fetch real production logs for bug fixing. And all those things are try to serve one purpose, which is setting up your codebase so that your agent can fix bug, building new features, verify things are working fully autonomously end-to-end. I put all skills inside AI Builder Club so you can copy-paste and ask your agent to customize for your own codebase. I put the link in the description below so you can join and access. And once you set this up, even though you don't use Symphony, this still going to be really useful. But, after that, this is where we can start setting up Symphony, connect to Linear, as well as this workflow.md file. So, once you clone the Symphony repo, you'll see folder like this. You'll have this folder of Elixir.

[00:07:39]So, this is one version implementing Elixir programming language from OpenAI.

[00:07:44]And most of the time, you can just use this Elixir directly. But, if you want to customize it to like connect not Linear, but connect to Trello or Jira, you can ask coding agent to customize it or even building a different language by pointing to spec.md file. And here's basically what I did in Python folder. I just point to spec.md file and asked it to build a new version in Python. But, most of the time, you actually don't need to do that. You can just reuse what OpenAI provided. And firstly, you can confirm whether the script is So, you can run script by doing this, which point to the Symphony program that has been built. And run help. So, this should show you the actual command about how to run Symphony. You basically just do Symphony and point to a path to workflow.md file. And if you can't just run the Symphony like this, you can run this to bind Symphony command to the specific path. So, just run this. And then you can do Symphony, point to a specific workflow.md file. And default, it will give you this warning. Then you can add this argument to the command, which will set up our Symphony background process like this. It will track all tasks, show you the project and next refresh time. It will track a specific Linear project you set up every 30 seconds. If there any ticket in to-do, it will pick up and show up in this list. And all those configurations are actually defined in workflow.md file. So, in workflow.md file, at the front matter, there is a project log.

[00:08:58]And Symphony script will basically read read that metadata, importing from a specific project. Same thing for all the other configurations, like how frequently it should pull the ticket data, what are things it should do after setting up a new workspace, how many agent can be run at the same time, and the Codex configuration. But, once you set up this basically monitoring the specific Symphony repo with Elixir implementation. What do you want to do is apply this to your own workspace.

[00:09:22]It's actually pretty straightforward.

[00:09:23]You can just open any coding agent like Codex or Cloud Code, point to the spec.md file and say, "I want to set up Symphony for my repo, and we'll reuse the Elixir implementation here, and help me build the workflow.md file for my repo." With this just one command, coding agent is smart enough to look at your own repo and design a workflow.md file inside there. And this is the one it created for me, including the project log and API key and all the other configurations. But, you do need to set up Linear first. If you haven't created Linear account yet, just go create a one and then add a new project. And in this project, click on the button here, you can just paste into your coding agent.

[00:10:00]The thing in the middle here is a project slot, or you can manually paste in to the workflow.md file as well. And meanwhile, you need to get a linear API key, which you can get by clicking on settings, security and access, and add a new personal API key here. And once you did that, you should run this command, which will save the linear API key globally on your computer. So, every time when agent try to use linear, it can access any projects you have access to. And there are some configurations you should do, which is status. So, Symphony out-of-box are designed for some special status control flow, like human review status and also merging status. Once you put a ticket into do, Symphony will automatically pick up and put that in progress and trigger an agent session. And once agent finish the work, it will change to human review status, so that you can review the work.

[00:10:42]And once finished, you can set the status to be merging, which will trigger the agent automatically raise a PR from this work. And once you did all that, you can do your own Symphony, pass through your workflow.md file, plus this I understand that this will be running without the usual guardrail comment. And now Symphony will be working and picking up all the tickets in your project here.

[00:11:00]To make it easier, you can also create a new view, set up this board, so that you get this kind of Kanban experience. So, just to test, I can just create a ticket, change the landing page hero copy from your company on autopilot to your AI growth team, and the set up the status to be to do. And this should trigger our agent here. If I go back here, you can see this time it pick up this ticket, and they can see the agent session show up, and then last agent message here. And depends on your settings, you can also go check this workspace. You can see inside this workspace, it has one workspace per ticket. So, each one is running isolated environment. And this example implementation also has uh kind of web UI dashboard, you can visit, and this will list out similar information you will see in terminal here. Not particularly useful, but I just thought I'd mention this. And you can see after a while, this agent changes ticket to in progress status, which reflect in our linear board as well. And if I click on that, agent made a plan and log all the steps they should do. After a few minutes, the agent check off every single items on the checklist, and upload a video recording to verify things are working. And as a human, I can just very easily see if things are working or not. And once I mark something as merging, it will also create a PR for me. So, this is a whole end-to-end process, and how you set things up. It definitely feels like future. If you hit any blockers, I have more detailed step-by-step breakdown, as well as all skills posted in the AI Builder Club. Every week, we have workshop to go through those latest learnings and answer any questions. So, if you're interested, you can click on the link below and join our next batch.

[00:12:26]But, this is project Symphony, how it works, and what's the implications. If you found this video useful, please give me a subscribe and comment below. Thank you, and I'll see you next time.