Building innovative enterprise agentic AI systems on Vultr at the Agent Arena Hackathon. Vultr's composable cloud platform is strengthened by its ecosystem of AI builders and developers. It was on full display recently when Vultr Llc teamed up with Cerebral Valley for the Agent Arena Hackathon in San Francisco. Successful agentic AI relies on two pillars: the agents themselves, and the infrastructure supporting them. Agents have progressed to a point that requires better infrastructure. That was the focus of the Agent Arena Hackathon. Using tools like Vultr Serverless Inference, Vultr Cloud Compute, and VM backends, competitors applied Vultr infrastructure to build production-ready enterprise agentic systems. Here are the three winning projects: Nusa: An agent that rewrites inference kernels for Vultr VX1(TM) Cloud Compute Nusa is a project built to make AI models run faster on the CPU servers that a user already has. Once the user picks a model, Nusa rewrites its slowest kernel to run on a user's exact CPU, then returns the faster version of that model. Nusa is an LLM running on Vultr Serverless Inference. In the hackathon, Nusa tailored models for Vultr VX1 Cloud Compute, reading a profile of the model and rewriting the kernel that took the most time. It's built to run unattended, with every attempt running in a throwaway microVM with no network, and it resulted in up to twice-as-fast decode time on AMD's Llama 3.1 8B. Read more about Nusa, from Alazar Shenkute, here. Skeleton Key: Fast, cheap browser automation for sites with no API Skeleton Key gives agents a way to access the websites you use every day - even the ones without an API. Vultr is the entire control plane, with Vultr Cloud Compute VMs, Vultr VPC, Vultr Serverless Inference, Vultr Container Registry, Vultr Object Storage, and more. If a user wants their agent (such as Claude Code or Codex) to access their Luma account, it can't do so, as Luma lacks an API and MCP. So Skeleton Key turns the website into an API the agent can call: It walks the site, clicks by numbered element IDs, records the site's requests, turns each request into an operation, and runs and verifies the operations in a sandbox. Results can be published as an MCP, REST, OpenAPI spec, and downloadable Python; personal data is redacted. Learn more about Skeleton Key, built by Carl Okpala and Jiyun Park, here. RL Forge: Turn any paper into a reinforcement learning environment With RL Forge, users can upload a paper or specification, and the system builds exercises with a standalone evaluation runtime for the agent to learn through trial and error. RL Forge uses document extraction, task generation, sandbox execution, and reward verification to create an environment where agents learn by doing. It improved accuracy from 2/20 to 5/20 on a mathematics evaluation and improved successful engineering repairs from 3/20 to 7/20 on an eight-turn LEGO bridge environment. Learn more about RL Forge, by Vishnu Vennelakanti, here. These projects all reinforced the great potential of agentic AI - provided it's built on infrastructure that can support it. Vultr provides that critical layer, and Vultr Llc were excited to demonstrate it with the innovative developers and builders who will power the agentic enterprise.

Vultr at All-In Summit 2026: connecting the future of Enterprise AI Infrastructure. The All-In Summit brought together technology leaders, investors, and operators in Los Angeles to explore AI, infrastructure, and the future of enterprise technology. Bringing partners together. As part of its sponsorship of the Frontier Tech Hub, Vultr brought together six technology partners - HPE, DDN, Moody's, AMD, Qdrant, and LegionEdge - to share perspectives on the technologies and infrastructure shaping enterprise AI. Partner-led sessions explored topics ranging from AI infrastructure and data management to benchmarking and specialized models, and were separate from the All-In Summit's curated main-stage content. Vultr CMO Kevin Cochrane kicked off the sessions with "Beyond Hyperscale: The New Era of Enterprise AI Infrastructure," examining how enterprise AI is moving beyond training toward inference and agentic AI. AMD's Aleks Shargorodskiy discussed AMD's approach to AI infrastructure in the "AMD IS ALL-IN ON AI," covering compute, networking, intelligent systems, and open software. DDN's Brian McCloskey focused on the data behind AI workloads in "The Data Engine Powering the AI Economy," including GPU utilization and the cost of running AI infrastructure. From benchmarks to specialized models. The sessions also looked at some challenges that come once the infrastructure is in place. Qdrant's Daniel Azoulai presented "Enough with the Bad Benchmarks," explaining why Qdrant built a large dataset with deep ground truth to improve benchmarking for vector search. LegionEdge CEO Sean Filimon covered two topics: how memory should work across the different layers around a model, and how specialized models are shaping the next stage of AI development. HPE's Geeta Chhabria discussed "Every Watt, Every Token: HPE's AI Infrastructure Advantage," focusing on the infrastructure needed to run AI efficiently. From AI infrastructure to benchmarking, the conversations at the Vultr Frontier Tech Hub covered a wide range of technologies shaping enterprise AI. Vultr is grateful to its partners for joining Vultr at the All-In Summit and sharing their perspectives with this community. Catch Vultr at another event or get in touch today.