H2O.ai expands H2O Super Agent™ platform to support autonomous, self-evolving agents with NVIDIA

H2O.ai is advancing the next generation of enterprise AI with h2oGPTe and H2O Super Agent™, a platform leveraging NVIDIA Nemotron open models and designed to build and operate long-running agent systems that plan, reason, forecast, and execute across complex enterprise workflows.

As organizations move beyond copilots toward sustained, multi-step autonomous systems, AI workloads increasingly require structured orchestration frameworks, scalable inference infrastructure, and secure runtime environments.

To support this shift, H2O.ai is strengthening its collaboration with NVIDIA through the integration of NVIDIA Run:ai intelligent GPU orchestration, while expanding the H2O Super Agent Agents Builder to support emerging agent architectures.

Together, these capabilities position H2O Super Agent as a unified platform for building, deploying, and operating long running autonomous agents at enterprise scale.

1. H2O Super Agent + h2oGPTe: A Platform for Agent Development and Orchestration

H2O.ai’s platform combines generative reasoning, predictive intelligence, and agent orchestration into a unified environment for building enterprise AI systems.

At the center of this architecture is H2O Super Agent, which coordinates multi-agent workflows capable of decomposing complex objectives into structured execution steps.

Inside the platform, H2O Agents Builder enables developers, data scientists, and enterprise teams to construct agent systems using leading open orchestration frameworks, including:

• LangGraph
• OpenAI Agents SDK
• CrewAI

These frameworks allow teams to assemble modular agent systems that combine reasoning models, tools, data pipelines, and predictive analytics.

The platform integrates directly with H2O Driverless AI, enabling predictive models to participate inside agent workflows for forecasting, anomaly detection, and decision optimization.

Together, h2oGPTe and H2O Super Agent enable organizations to move from AI assistants to systems that can plan, reason, and execute across enterprise environments.

2. Scalable Agent Execution with NVIDIA Run:ai

As agent systems become more capable, managing the infrastructure that powers  them becomes increasingly complex, often leading to underutilized GPU resources, higher operational costs, and inefficiencies that make AI investments less effective. Many agentic workloads need dynamic scaling, but fixed GPU configurations leave expensive accelerators idle, reducing overall return on investment and limiting scalability.

Deep agents may run for minutes or hours, coordinate multiple models, and dynamically invoke tools across enterprise systems. Supporting these workloads requires intelligent orchestration of GPU infrastructure and inference services.

H2O.ai integrates NVIDIA Run:ai, an enterprise platform for AI workloads and GPU orchestration, into the Super Agent platform to enable:

• Dynamic scaling of inference workloads
• GPU resource allocation and workload prioritization through NVIDIA Run:ai APIs
• Efficient orchestration of NVIDIA NIM microservices and Nemotron models
• GPU fractioning and resource sharing across teams

By integrating NVIDIA Run:ai, H2O Super Agent is designed to enable dynamic scaling of compute resources as agents execute multi-step workflows, improving GPU utilization while maintaining predictable performance.

The result is a production-ready infrastructure layer for deploying deep agent systems across enterprise environments.
 

3. Expanding H2O Agents Builder with NVIDIA AI-Q

As long-running agents evolve, orchestration patterns are becoming increasingly sophisticated.

H2O.ai plans to expand Agents Builder to support the NVIDIA AI-Q Blueprint for building deep research agents. 

Part of NVIDIA Agent Toolkit, AI-Q blueprint introduces architectural patterns designed for agents that must plan, reason, and execute over extended time horizons. These systems incorporate capabilities such as:

• Intelligent and dynamic routing
• Full Customization, enabling organizations to adapt agents to their unique use cases.
• Centralized Storage and File System
• Long-context memory management
• Secure sandboxed skill execution
• Integrated evaluation and observability

By integrating with the NVIDIA AI-Q Blueprint, H2O Super Agent empowers enterprises to build customizable deep research agents that they own and control while integrating predictive intelligence and deploying within sovereign enterprise environments.

Because AI-Q is built on LangChain deep-agent architecture, it aligns naturally with the open orchestration frameworks already supported within H2O Agents Builder. This expansion will allow organizations to develop their own long-running autonomous agents while maintaining control over models, data, and infrastructure.

4. Preparing for Secure Autonomous Agents with [NVIDIA NemoClaw]

As AI systems become more autonomous and self-evolving, security, governance, and runtime safety become critical requirements.

We’re working together on NVIDIA NemoClaw — an open source stack that simplifies running OpenClaw always-on assistants, more safely, with a single command. As part of the NVIDIA Agent Toolkit, it installs the NVIDIA OpenShell runtime—a secure environment for running autonomous agents, and open source models like NVIDIA Nemotron.

 NVIDIA OpenShell introduces a runtime architecture designed to safely execute agent-generated code and tools within isolated environments. By integrating with NVIDIA Agent Toolkit and NVIDIA OpenShell, H2O can support safer execution of autonomous, self-evolving agents in enterprise environments, such as:

• Sandboxed execution environments for agent skill development
• Policy-based access controls and guardrails
• Secure file system and permission management
• Secure integration with enterprise systems

This approach moves agent safety outside the model itself and into the runtime environment, ensuring agents cannot exceed their approved permissions.

H2O.ai supports deployment architectures designed for sovereign and regulated environments, including air-gapped and on-premises configurations, enabling enterprises and governments to retain full control over data, models, and execution. Integrating with  NVIDIA Agent Toolkit will help organizations run long-running autonomous agents safely across regulated industries and government environments.

Enabling Deep, Long-Running Agents

Deep agents differ fundamentally from single-response AI systems.

They:

• Decompose complex objectives into structured sub-tasks
• Coordinate parallel specialist agents
• Integrate predictive models into reasoning loops
• Track progress across extended workflows
• Adapt execution dynamically based on context

These systems may run for extended periods while coordinating multiple models, data systems, and infrastructure resources.

Supporting that level of execution requires a complete platform spanning agent orchestration, scalable inference infrastructure, and secure runtime environments.

H2O Super Agent brings these components together to enable organizations to move from experimentation to operational AI systems.

Closing

The next phase of enterprise AI will not be defined by individual models, but by systems of agents that can reason, plan, and execute across entire organizations. By combining H2O Super Agent’s orchestration and predictive intelligence with NVIDIA AI infrastructure and emerging agent architectures such as NVIDIA AI-Q and NVIDIA OpenShell, H2O.ai can help enterprises build a foundation for deep, long-running AI systems that operate reliably at production scale.

H2O.ai continues its collaboration with NVIDIA across accelerated computing and AI software initiatives, including prior integrations of Nemotron open models and NVIDIA NIM microservices.

H2O.ai is also part of the NVIDIA AI Factory for Government reference design.