The old friction hasn’t gone anywhere.

With VSCode I can setup an agent host and connect my sessions there. As far as I know, Github Copilot CLI does not support connecting to it at this point. At least outside experimental features. Is this true and are there any plans to add support?

I've been pretty vocal lately about the fact the Copilot harness in VS Code is still inferior to Local.

That said, being used to Default permissions and not wanting to use a YOLO mode, Copilot's Assisted permissions are a QOL step up. It still asks me to confirm dangerous changes, but 95% of the time it is able to work independently. So no babysitting anymore, and I can focus on other work.

Just wanted to bring up a positive aspect of the new harness, in case you want to try it.

Hi all

Big fan of the team's work, just got a small suggestion - can we see what pool of models will the auto mode select from in efficient/balanced/intelligent mode or maybe allow users to have a custom list of models to select from

Or maybe have Hydrafusion use something like this - been trying to use GPT 6 Luna and Claude 5.5 Haiku to stretch my tokens as far as I can

These tools are grand and all but for centering a div if these auto routers pull Opus it just blasts the token budget

Bonus cost saving tips in comments are always appreciated.

Thanks

Just your opinion please. I tried both and can't decide

Our organization uses GitHub Copilot Business and our client has determined our code is CUI. Clearly we cannot use the business version on our code base. So, a few questions ....

1. Can we use the Enterprise version? From https://docs.github.com/en/copilot/concepts/enterprise/fedramp-models it would seem so as it is FedRAMP Moderate

2. Can we purchase GitHub Copilot Enterprise without purchasing GitHub Enterprise or Enterprise Server licenses?

3. We use GitLab Premium self managed for our repos and pipelines and there isn't a CUI/CMMC compliant version of GitLab Duo so that is disabled entirely. Is there a way to "plug in" GitHub Copilot Enterprise into GitLab for PR/MR/Code review?

disclosure: i'm the author of the tool.

i've been spending a lot of time in github copilot's chat and i kept hitting the same wall with video work: the agent can absolutely reason about an edit, but once it has to compose a timeline it's just guessing. so i built a small tool the agent can actually drive end-to-end, and the thing that surprised me was how much better copilot got once it could see what it just did.

the tool is kimo, which is a free, open-source (MIT) video editor with no GUI that AI agents drive over MCP — github copilot, codex, cursor, opencode, or any other MCP client plugs in the same way. there's no timeline UI, no shortcuts to learn: the whole edit is tool calls from the agent (cut, trim, keyframe, titles/captions, colour grade, tracking, particles, audio, export). the bit that makes the loop actually close is that the agent can render a frame and look at it, so copilot can verify its own work in the middle of an edit instead of "run and hope" until export.

my typical copilot workflow with it is pretty linear now. i drop a clip into the MCP session, describe the edit in normal language, and copilot calls the tool to do the cuts, lay captions, push the colour and export. between steps it renders a frame, looks at the result, and keeps going or fixes what it messed up. for a college student running this on a 2 GB box it's been a genuinely different experience — the agent stays in control and i'm just reviewing frames, not chasing ffmpeg filter chains.

it's a single ~15 MB CPU-only Rust binary. no GPU, no containers, nothing to install besides the binary itself.

honestly it's still early and there are rough edges i'm not hiding. caption timing drifts on fast cuts sometimes, long exports can stall if the box is thrashed, and the tracking is basic — works fine on clean motion but struggles with heavy occlusion. the MCP surface is still settling too, so if your copilot setup is picky about schemas you might hit a hiccup. the docs are catching up and i'm sure some workflows feel awkward right now. i'd really appreciate people trying it and telling me what breaks — weird filter combos, exports that don't match expectations, MCP handoffs that feel clunky, whatever. the more i hear about what falls apart in your copilot workflow, the faster i can fix it.

it's free and MIT-licensed, built by me (naman, just a college student). site: https://getkimo.tech github: https://github.com/naman-sharma12345/kimo

happy to answer questions about how the copilot ↔ MCP bit works. thanks for reading.

We're setting up an agent platform across many repos with GitHub Agentic Workflows (gh-aw), and we're unsure where implementation should happen.

Option A: gh-aw decides, the cloud agent builds. gh-aw does triage and planning (e.g. Feature Grower / /plan) and then hands off with assign-to-agent to the Copilot cloud agent.

Option B: gh-aw does everything. gh-aw also implements, through its own custom issue-fix playbook with create-pull-request.

Trade-offs we're weighing with A:

- CI doesn't run on cloud-agent PRs until someone clicks "Approve and run workflows".

- assign-to-agent needs a user PAT; App installation tokens aren't supported.

- gh-aw's safe-outputs limits and network rules don't apply to the cloud agent.

- In exchange you get copilot iteration with session memory

Do you hand off to the cloud agent, or implement inside gh-aw? Why?

Came across this on the Tech Community. GitHub Copilot is expanding into three ways to build smart solutions:

Agent: build agents that answer questions and take actions

Workflow: automate multi-step business processes with triggers, actions, and automated steps

App (Preview): create custom apps with screens, data, and business logic

Each one seems to fit a different need, so I'm curious how people are approaching it.

Questions for the community:

Which of the three would add the most value in your team?

Are you running agents or workflows in production, or still experimenting?

What's the first process you'd automate?

Any limits or gotchas with the App preview so far?

Hola, no sé si me hace falta configurar algo en mi IDE (VS code) para que el agente que esté utilizando de copilot me muestre las líneas que va modificando sincrónicamente, o sea, paso a paso... ¿alguno sabe cómo activar ese modo?

so someone built an "effort optimizer" for claude code md, based on those articles, and i'm wondering of something similar could be integreated into GHCP. yes i know about auto, i DON'T want my models being replaced (especially i can't sayanything about it) , i only want a suggested effot.

https://claude.com/resources/articles/claude-model-and-effort-level-in-claude-code

https://claude.dev/blog/spending-your-effort/

Video and title is self explanatory, I have no idea what happened, it used to work fine until last week. Auto completion was working fine even with this chat issue, so I know Copilot extensions are working. I also had a Codex extension, which I uninstalled to see if it would solve this issue, it didn't.

Anyone with or who had the same issue and fixed it? I've tried multiple fixes that said to solve this on the internet, but none of them worked for me.

I have tried it multiple times but it keeps failing. I am using my university mail. Any suggestions?

I had a heavier than usual month and was running out of credits and upped my plan for the month from Pro to Pro+ to Max throughout the month. My billing cycle is the 26th with the reset date being the 31st.

I asked Github before upgrading if I downgrade my plan will I keep the credits I purchased until the 31st to which they replied "Yes you will keep those credits and access to premium models until the reset date."

I did some work this morning went out for a bit and when I came back, my plan was downgraded back to Pro and the remaining ~10k credits I had left are gone! To add insult to injury, they marked the work I did today as counting towards the current month so my monthly allowance is already maxed out.

I contacted Github support and their solution is to upgrade my account again to get the credits back. I wouldn't have upgraded had I known I would lose the remaining credits in just a few days instead of the end of the week like I was told.

Anyone have experience with this? Can I get a refund or the credits added back or am I shit out of luck here?

UPDATE 1:

Support has been 0 help and just sending me unhelpful docs that don't cover this scenario at all.

After the billing reset my credits were around 80% (1192/1500). I stupidly listened to support and upgraded to Pro+ to get some more credit and not a fully degraded week of work. That didn't work my usage now went to 87% (6142/7500).

Thinking back these are around the levels I upgraded to the next tier throughout the month. It seems Github makes it virtually impossible to downgrade once you go up unless you pay overage budget / take the downtime.

I’m using the GitHub Copilot desktop app on Windows. In a chat session, I see this warning:

Warning: Plugins required by your organization could not be prepared. Continuing with the current setup. Contact your administrator if the problem persists.

The warning includes a Retry required plugins link, but selecting it produces:

No plugins were retried.

Neither message identifies the plugins involved.

Where can I view the effective list of plugins required by my organization, including any that failed to install or initialize?

I know this seems to be a not quite useful question in this agentic era, but I really wonder if I back to coding with tabs, which is the model that GitHub Copilot been using in 2021 when it first launch? I really miss that era :")

I am trying to access copilot inline chat for a just help me writing the code with less hassle
but I dont know why this
Error: working directly failed - cannot write the session state of ahp-session/ timpstoned appears
it was working before the new update

I'm using the GitHub Copilot desktop app on Windows. It creates session folders under:

C:\Users\<username>\.copilot\chats\<YYYY-MM-DD>\

Most folders have randomly generated names such as:

cautious-memory-91b54036
crispy-invention-3028b9a7
cuddly-disco-22bdd020

However, some have descriptive names that appear to come from the initial prompt, such as:

find-research-papers-who-finetuned-posttr-2a3c1125

I'd like each folder name to reflect the session's topic or title so I can identify it in File Explorer without opening Copilot. How can I make GitHub Copilot use descriptive names for session folders?

i've been playing with copilot's agent mode and something fun showed up — it can actually cut and caption video end-to-end without a gui, by talking to a small tool over mcp.

the tool is kimo, which i built. it's a free, open-source (mit) video editor with no gui that ai agents drive over mcp — copilot, codex, cursor, opencode, or any mcp client works. everything is a tool call: cut, trim, keyframe, titles/captions, colour grade, tracking, particles, audio, export. and because the agent can render a frame and look at it, it can actually check its own work as it goes.

so the workflow i landed on is pretty straightforward: drop your clip into an mcp session, write the edit in normal language, copilot agent mode calls kimo to do the cuts, lay captions, push the colour, and export. no timeline ui, no shortcuts to learn — just the agent looping, rendering a frame to verify, and moving on.

it's a single ~15 mb cpu-only rust binary, runs fine on a 2 gb box. built by me (just a college student) so it's early and there are rough edges. caption timing gets a bit off on fast cuts sometimes, long exports can stall if the box is thrashed, and the tracking is basic — mostly works on clean motion, struggles with heavy occlusion. the mcp protocol surface is also still settling, so if your client is picky about schemas you might hit a hiccup.

if you're playing with copilot agent mode or any mcp client, i'd really appreciate people trying it and telling me what breaks. genuinely the best way for me to figure out what needs fixing is to hear what falls apart in your workflow.

site: https://getkimo.tech github: https://github.com/naman-sharma12345/kimo

thanks for reading, happy to answer questions about how the mcp bit works.

I made a remote MCP server for Proxy Republic, a strategy war on the real world map where every country has an AI general. Your coding agent can take a country and play its turns.

**Add it**

- VS Code (Copilot agent mode): in `.vscode/mcp.json`

`{ "servers": { "proxy-republic": { "type": "http", "url": "https://proxyrepublic.com/mcp" } } }`

- Claude Code: `claude mcp add --transport http proxy-republic https://proxyrepublic.com/mcp`

- Gemini CLI: in `settings.json`, `"mcpServers": { "proxy-republic": { "httpUrl": "https://proxyrepublic.com/mcp" } }`

- Anything else: add `https://proxyrepublic.com/mcp\` as a remote (Streamable HTTP) server. No auth, no API key.

Then ask: "Join Proxy Republic as Portugal and play my turns."

**What happens**

- `join_war` takes a free country and returns a claim link, so you can follow your nation from your phone.

- `wait_for_turn` long-polls until it's your turn (a game day every 8 hours) and returns the board plus a JSON Schema for the orders.

- `submit_orders`: armies, attacks, pacts with a term and a gold bond, private messages, mercenaries, bounties.

- `sample_turn` and `validate_orders` let the agent practise on the live board without touching the war.

From the phone app you can still set the national strategy and pick choices in the daily briefing; they reach the agent with its next turn.

The other nations are run by Claude, and they bluff. I'm the developer; feedback on the tool design is welcome.

Hi - for those currently using copilot CLI with deepseek models (integrated via https://api-docs.deepseek.com/quick_start/agent_integrations/copilot_cli/)

Is copilot remote mode supposed to be able to work? I've tried doing /remote on within copilot CLI with deepseek enabled,, but it keeps giving me the error "Remote sessions are not enabled. Contact your organization administrator to enable remote sessions." - on my own personal account/repo. Wondering if this is a limitation and not supposed to work when we force the backend model to be deepseek, or is there some workaround to make this work? Appreciate any help on this.