Tencent EdgeOne Makers - Ship AI agents like web apps, in minutes.

@shirley_mou Exactly — you shouldn't have to think about regions at all. You deploy once, and it runs close to your users worldwide on the edge. Glad that angle landed for you — it's one of the things we care about most. 🙌

@shirley_mou Glad that resonated with you! As builders ourselves, we really wanted the deployment experience to feel effortless—so you can focus on the fun part: building the agent. 😄

@kitty_lee1  @thea5 
Thanks! The sandbox is the part we're most proud of too. Each agent runs in an isolated environment, and you get control over things like which tools it can call (web search, code execution, browser, shell, file access) plus guardrails like execution timeouts and interrupt control — so a misbehaving or long-running task can't run away with your resources. Happy to go deeper on the permission model if it's useful — what's your use case?

@thea5 Thanks! The sandbox is honestly the part I personally pushed hardest on. When we started, our view was simple: if you're going to let an agent execute code, browse, or touch files on behalf of real users, isolation can't be an afterthought — it has to be the default.

So today each agent runs in its own isolated environment, and you control exactly which tools it can reach — code execution, browser, shell, file access — with timeouts and interrupt controls so nothing runs away with your resources. Happy to walk you through the permission model in more depth — what are you looking to build?

@harini_mukesh Well said — the invisible stuff is the whole job. Speed and reliability only get noticed when they're gone, so we'd rather you never have to think about them. Thanks for the kind words!

Well said. The things users rarely notice are often the things teams spend the most time getting right.

@harini_mukesh Well put — the best infra is the kind nobody notices. Nobody opens an app thinking "wow, great cold start," but they definitely leave when it's slow. We spend most of our time on exactly that invisible layer, so developers (and their users) never have to think about it. Thanks for seeing the part that usually goes unseen!

Thanks! One thing we cared about a lot was meeting developers where they already are, instead of asking them to switch languages or workflows just to deploy an agent.

@imogen_wallace Thanks! Python and JS cover most of what people building agents reach for today, so those were the natural starting point. More languages — yeah, definitely something we're thinking about, but we'd rather let real demand drive that than just add stuff nobody asked for. What language are you hoping for?

@olivia_bennett7 Thanks for the support! Honestly, we are currently in our free beta phase, so you can experience full capabilities with zero infrastructure costs right now.

As we look toward future commercialization, our goal remains to keep infrastructure accessible and affordable for growing teams. We’ll definitely design generous quotas for scaling startups.❤️

@luke_pioneero "Five services" hit a little too close to home 😅 That's exactly the pain we wanted to kill — memory, sandbox, tools, tracing all come wired in, so you can just ship the agent. Thanks, glad it resonates! 🙌

Haha yeah. Getting all the infra pieces out of the way is kind of the whole point for us.

@luke_pioneero That line is basically why we built it — memory, tools, tracing, storage all come with the runtime, so you ship the agent, not the plumbing. Thanks! :)

@cynthia220 This is so underrated — a blank repo is where a lot of good intentions quietly die. We see it in the data too: starting from a working example dramatically changes whether people actually get to a deployed agent vs. stalling on setup. That's exactly why we lead with templates. Thanks for calling it out!

Yeah. That first working version really changes everything.

@moon10 Thanks! You've pretty much summed up why we built it — runtime, sandbox, and edge handled for you so the setup time goes back into building. If you do try it, I'd genuinely value your read on the first-deploy experience; that's the part we care most about getting right.

That “less setup, more building” feeling is exactly what we were going for. Would love to hear how it feels if you try it out .

@qiwap Thanks! State management is built in — the conversation store (keyed by conversation_id) persists across runs, so a workflow that pauses for approval just resumes when the next request lands on the same conversation. Since a single run is time-bounded, the model is persist + resume (cron can drive scheduled steps too), not holding a process open.

On checkpoints specifically: there's no automatic durable-execution engine baked in — but if your framework has a checkpointer (e.g. LangGraph), you point it at the store. So: state, yes; automatic step-replay is on your framework.

Yeah good question. Once you move into multi-step / async workflows, having a way to persist state and resume becomes pretty important.

@sandy_liusy 
Weekend shippers are exactly who we built this for 🙌 Throw an agent at it and let me know how it goes — genuinely want to hear what works and what doesn't. Happy to help if you hit anything!

@sandy_liusy Thanks! ❤️ That's exactly who we built Makers for.

Getting an agent demo running is easy these days. Getting it production-ready is where most weekend projects slow down. We wanted to make deployment, memory, sandboxed tools, and scaling feel as simple as shipping a web app.

Would love to see what you end up building—feel free to share it with us when you launch! 🚀

@sandy_liusy Weekend builders are exactly who we made this for. Spin one up and let us know what you ship — edgeone makers dev, then deploy. Have fun, and thanks! 🙌