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Open-source AI platform provider H2O.ai believes a blend of generative and predictive AI models makes for more consistent responses which enterprises want from an AI agent.
H2O.ai launched its new multi-agent platform that blends generative and predictive AI and is now generally available.
The platform, h2oGPTe, uses the company’s AI models Mississippi and Danube, but can also access other large and small language models available. The company said h2oGPTe works in air-gapped, on-premise and cloud systems.
Sri Ambati, founder and CEO of H2O, told VentureBeat that having both generative and predictive AI gives enterprises more confidence that the agents will work exactly as they need without compromising security.
“The number one problem with agents is consistency. Can I get a consistent response from an [large language model] LLM for the same prompt? I think you get two different, like multiple responses right now,” Ambati said. “But you can bring multiple models that negotiate, plan and deliver an outcome. Think of it as humans can have a bit of variability with each other, but you still expect a consistent response, and that’s the domain of predictive AI combined with generative AI.”
Ambati explained that generative AI models are “decent at content generation and very good at code generation,” but predictive models bring more scenario simulation to the table. He said the predictive models bring consistency to agentic responses because these do not just generate responses but learn from patterns in data.
The platform is built for finance, telecommunications, healthcare and government enterprises that need to manage multi-step tasks. H2O.ai’s agent works best for organizations that want to get insights into their business and not just a guide that runs through their workflows. This is because agents within the h2oGPTe platform can read multimodal data like charts and craft answers to questions like “Should my company sell more dolls this year?” that consider the enterprise’s historical financial data or market trend information they store.
Multimodal agents
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