A hypothesis: agents don't need a sandbox. They need a platform that thinks, acts, remembers, and improves as one system. There's a debate running this week about sandboxes. One camp says sandboxes are dead and the future is a private cloud for every agent. Another says a sandbox is a compute primitive, like Postgres or Redis, and primitives don't win markets, platforms do. A third says sandboxes are a winner-takes-most market. I think all three are arguing about one layer of the problem. My hypothesis is that agents are the first workload that exercises every layer of a platform inside a single loop. They think, they act on real systems, they remember, and they improve. A web server touched compute and maybe a database. An agent touches inference, execution, tools, storage, retrieval, telemetry, and evaluation, in one task, often in less than a minute. So what an agent needs isn't a better sandbox. It's a platform where those four things run as one system. If that's right, the unit of competition isn't the sandbox and it isn't the model. It's the integrated cloud for agents. Whoever builds one that's compelling across agents, data, and model access at the same time wins. Let’s start with how an agent actually runs. One “prompt” fans out into a dozen model calls, a web search, a database query, a code execution, a tool call with a credential, a checkpoint, and a trace. Today a typical team buys those from five or six vendors and glues them together. Six vendors means fifteen seams. Every seam is an integration someone maintains, a credential someone stores, a network hop that adds latency, an egress fee, and a place where the trace dies. Point solutions are excellent but partial: most sandbox companies don't run inference, and model companies don't manage agent memory or observability. The loop is what breaks on a stitched stack. Models have to be routable, and routing only works when inference sits next to the agent. Every few weeks a new model tops a chart. Open-weight models went from <20% of token volume on our Inference Engine in April to >85% by August. My read: the model is the most swappable layer in the stack, so you want a router in front of it, not a hard-coded choice. We built an Inference Router for that. On Kimi K3 we see an aggregate cache-hit rate above 90% across our own workloads. The router has to know what the inference layer has cached, and the cache has to sit next to the sandbox that's going to need it. That's a function call on one platform. The agent and harness layer is where the business logic lives, so the runtime has to be built for it. I believe the harness, its prompts, its tool permissions, its memory, and its evals will hold more of a company's business logic than the model ever will. The model is rented. The agent is yours. So we built Managed Agents from the ground up as purpose-built primitives, not VMs with an agent sticker on them. Every session gets its own Firecracker microVM, resumes from pause in a few hundred milliseconds, and is billed for active CPU at $0.044 per vCPU-hour, so the wait costs zero. Action Gateway gives agents governed access to 20K+ tools across 600+ providers through one MCP endpoint, matched intent to tool with 99%+ accuracy in our testing, and brokers credentials at call time. The agent never sees the key. You can only make that promise when the runtime, the gateway, and the secret store are the same system. Data and recursive loops are what make an application more intelligent over time. This is the part I believe in most and the part with the least proof so far, because the products are weeks old. The hypothesis: two teams start with the same model and the same harness, and within a quarter they diverge, and the one with the loop wins. On DigitalOcean the loop runs on two services. Agent Data Services is one governed catalog where video, documents, logs, and agent sessions land agent-ready and searchable by meaning. Agent Learning Services closes the loop: Insights records every step, Signals flags the sessions where the agent stalled, looped, or lost the user, and Evaluations and Simulations test the fix before a customer sees it. In an internal demo, an optimized support agent went from failing to passing it with 90% fewer input tokens. Intelligence stops being something you rent via models and becomes something your application accumulates, tuned to the exact problem it's solving. And the whole thing has to go down to the floor. DigitalOcean builds the routing model and the microVM runtime operates the GPUs they run on, across 20 data centers with NVIDIA HGX B300 and AMD MI350X fleets. Much of the agent infrastructure market rents GPUs from someone who rents a data center from someone else, with margins stacked on margins. My view is that active CPU billing, a warm KV cache next to the sandbox, and a router that can see the inference layer are only possible when you control the data center, the hardware, and the software as one stack. The obvious objection is that "integrated" is a polite word for lock-in. I'd argue the opposite, and I think it's the real test of whether a platform is integrated or just bundled. Integration should be about removing seams, not closing doors. Bring Claude Code, Codex CLI, OpenCode, Hermes, LangGraph, or your own OCI image. Hit one endpoint compatible with OpenAI, Anthropic, and 60+ Open models. Bring your own key for a frontier model, or run open weights we host. Our routing model and the proxy under it are open source. The point is that the seams are gone, not that the exits are. One early signal that the hypothesis might be right: roughly 70% of our large AI customers have attached a one or more platform primitives. Agents come in through inference or a sandbox and pull the rest of the cloud behind them. That's what I'd expect if agents really do need every layer at once. So, back to the debate. The platform that wins won't be the one with the best sandbox, and I don't think it'll be the one with the best model. My bet is on the platform where an agent thinks, acts, remembers, and improves as one system. DM me if you are interested in trying our Agent primitives. #digitalocean #agents #Agenticloop #learning #Context #sandbox #Runtime #DOCN