H2O.ai, the leader in open-source Generative AI and the most accurate Predictive AI platforms, announced the industry-first convergence of Predictive AI and Generative AI in its enterprise generative AI platform, h2oGPTe. The new agentic capabilities enable h2oGPTe Agents to seamlessly integrate H2O.ai’s predictive AI models into autonomous workflows, ushering in a new era of operational efficiency and intelligent automation.
This breakthrough transforms h2oGPTe into the only end-to-end enterprise AI platform to converge Generative and Predictive AI capabilities in air-gapped, on-premise and cloud environments, ensuring both compliance and innovation. Built for industries like finance, telco, healthcare, and government, h2oGPTe’s multi-agent AI system autonomously manages complex, multi-step tasks, drawing from both generative insights and predictive accuracy to enhance enterprise decision-making with transparency and control.
“Multi-agent systems are the digital workforce of tomorrow, equipped not only to act but to adapt, collaborate, and evolve,” said Sri Ambati, Founder and CEO of H2O.ai. “Our pioneering work with agentic AI allows organizations to unlock the potential of converged predictive and generative intelligence—moving beyond automation to true transformation of enterprise workflows. It’s about amplifying human potential and democratizing AI, so every business can achieve exponential growth.”
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To further illustrate, an agent can classify customer call center inquiries into over 80 categories using a fine-tuned H2O Danube model at a fraction of the cost of traditional large language models (LLMs). This system is then orchestrated with an Agentic AI framework powered by state-of-the-art LLMs to dynamically provision operators using a predictive AI agent, enabling efficient complaint resolution.
“The development and deployment of generative language models, particularly in high-stakes sectors like banking, demand a rigorous framework that balances automation of testing and evaluation with human calibration to ensure reliability and transparency,” said Agus Sudjianto, Senior Vice President Risk & Technology.
Consistency and safety in Agentic AI needs rigor, continuous reinforcement, and learning. Building trust in AI agents requires rigorous testing, thorough evaluation frameworks, and transparency through open-source development.
SOURCE: Businesswire
