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New York — io.net, the world’s largest decentralized GPU network, today unveiled Agent Compute, a first-of-its-kind platform that allows AI agents to autonomously provision their own computing resources.
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With this launch, AI agents can spin up GPU clusters, run workloads, and scale resources dynamically—without the enterprise onboarding and procurement processes that have historically blocked smaller teams.
“The current cloud model is built for enterprise budgets,” said Gaurav Sharma, CEO of io.net. “Agent Compute removes that barrier. An agent can independently find the best-priced GPU for the job, provision it, and manage the infrastructure end-to-end—so developers can spend their time building, not comparing cloud pricing or configuring servers.”
Cost-Effective, On-Demand Compute
Unlike traditional cloud platforms like AWS or Google Cloud, which require lengthy onboarding, complex billing, and minimum commitments, Agent Compute sidesteps these hurdles entirely. AI agents can interact directly with io.net’s marketplace of over 10,000 GPUs across 138 regions in 130+ countries, accessing compute at up to 70% lower cost than conventional providers. Resources can be provisioned on demand and released immediately when tasks are complete.
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The platform leverages the Model Context Protocol (MCP), giving agents visibility into available compute resources—including GPU specs, costs, and availability—to make informed provisioning decisions. This helps prevent costly errors: previous incidents include an Amazon AI shopping agent that caused a 13-hour outage by deleting a production environment and OpenClaw users racking up $3,600+ monthly bills from runaway workflows.
“This is a step towards truly autonomous agents,” Sharma added. “Right now, agents still depend on humans for infrastructure. As they become more capable, that dependency becomes the bottleneck. If agents are going to operate independently—making decisions, executing tasks, scaling resources in real time—they need the ability to provision their own compute. That’s the future we’re building toward.”
Developer-Friendly Automation
In practice, developers can let an agent analyze data, spin up a GPU cluster, process the workload, and terminate resources automatically. No manual setup. No leftover costs. Spending limits and resource caps keep developers in control.
The implications extend beyond convenience. With autonomous compute management, the limiting factor shifts from infrastructure access to imagination. Solo developers can now build systems that previously required enterprise-level resources—agents that scale globally, process massive datasets, or deploy models on demand.
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io.net’s full-stack ecosystem combines the cost-effective, programmable infrastructure of io.cloud with the unified, API-accessible toolkit of io.intelligence, delivering a single platform for AI startups and developers to train models, run agents, and scale LLM infrastructure. Agent Compute is now available in early access, with broader rollout planned later this year.