AgentBox is live: the whole stack for production AI agents, in one place

Today, GMI Cloud is launching AgentBox. Here is why we are excited to see it go live.

If you have ever tried to push an AI agent into production, you already know the annoying part is not really the agent itself. You pull execution infrastructure from one vendor, inference from another, observability from a third, then you still have to figure out how people are supposed to find and use the thing you built. Every seam between those pieces adds latency, cost, and ops time that pulls your team away from the product.

Plenty of platforms solve one slice of that. AgentBox brings the whole loop together.

What it is

AgentBox is a place where teams can find ready-to-use AI agents and where builders can publish their own. An agent here is a focused AI worker that does one job well: one reviews code, another pulls from S3, SharePoint, Confluence, or Notion and builds a retrieval graph, another runs benchmark suites like MMLU and HumanEval. Behind each agent is a model plus the application code that makes it useful, and both run on GMI Cloud. GMI handles server setup, provisioning, and scaling for you.

That is the part I keep coming back to. Building the agent was never a hard problem. Running it in production and getting it in front of buyers was.

Two ways to deploy, depending on how much you want GMI to hold

This is the detail most launch posts skip, and it is the one that decides how you use the platform.

GMI CE Deployment (managed). GMI hosts your agent on our infrastructure. You open the Deploy Wizard from your dashboard, pick a region, and it builds your container, runs it, and assigns a public URL automatically. This is the path most teams should start on. Your container goes from dashboard to live URL in one flow, with the load balancer handled for you.

GMI MaaS (self-hosted inference). You keep hosting the agent yourself but route model calls through GMI Models as a Service. You bring your own runtime, we handle inference for 100+ models behind a single key.

You are free to choose your operating model. Start managed, move pieces in-house later, or run hybrid from day one.

The five things that make it click

100+ models, one API key. Claude, GPT, Llama, DeepSeek, Qwen, and more, all on network, all through a single inference key. One key covers every provider, and routing is handled for you. Model calls bill per token through MaaS, so you pay for exactly what the agent consumes.

A marketplace on day one. Publish your agent once, and enterprise buyers already on GMI can discover, evaluate, and deploy it immediately. The customer network comes with the platform, so distribution starts the moment you list. Pricing is usage-based: you pay per second of compute your agent uses, and listing is free to start.

Keep it private, or go public. Run an agent privately for your own team, or publish it to the marketplace when you are ready. Going public puts your agent in front of every enterprise buyer already on GMI, turns it into a revenue stream, and earns a trust badge that shortens the enterprise review process. Same agent, wider reach.

Trust badges buyers read. Every listing carries a badge that tells a buyer exactly who runs the infrastructure and how the listing was reviewed. Verified means GMI reviewed it, and it runs end-to-end on GMI infrastructure. Powered by GMI Infrastructure means it is hosted on dedicated GMI compute with the publisher supplying the model and API. Powered by GMI MaaS means it is self-hosted by the publisher but calls GMI for inference. For an enterprise security team, that distinction is the difference between a quick yes and a three-week review.

Isolated by default, built for enterprise. Every agent runs in its own environment. Tenant traffic is isolated, and resource quotas are enforced at the container level. Underneath sits dedicated NVIDIA H100, H200, and GB200 infrastructure, with data isolation and RBAC at the tenant level, plus real time logs, traces, and cost analytics. This comes standard on every plan.

Who this is for

If you are a builder, you can deploy agents to enterprise customers and lean on GMI for distribution. Bring an agent, deploy it on GMI, and list it for others to find and use.

If you run an engineering team, you can deploy agents for internal users on one isolated, observable, fully managed platform that keeps your whole stack in a single place.

And if you are just moving fast, start with a POC on the managed path and scale when you are ready.

A pattern worth knowing before you deploy

Agent tasks take their time. Multi step reasoning, document analysis, and model chains often run from 30 seconds to several minutes, longer than most HTTP gateways keep a connection open. The clean way to handle this is to decouple accepting the request from returning the result: accept the call, return a job_id immediately, run the work in the background, and let the caller poll a status endpoint until the result is ready. We wrote up the full pattern, with FastAPI and Express implementations, in the docs. Worth a read before your first deploy.

Try it

Browse the catalog, deploy your first agent, and list it, all from the console.

Start in Console: https://console.gmicloud.ai/user-console/agent-marketplace/browse-agents

Read the docs: https://docs.gmicloud.ai/agentbox-marketplace/overview