At the Open Data Science Conference (ODSC) in Boston, Zerve announced the launch of what it calls the industry’s first enterprise-grade multi-agent system for data and AI development. The release marks a major milestone in the evolution of AI agents from narrow assistants to full-fledged collaborators in the software development lifecycle.
Unlike traditional AI assistants or code helpers, Zerve’s Agents are embedded in a robust operating system purpose-built for complex workflows. The platform enables agents to plan, code, build, test, and deploy alongside human developers. With direct integration into enterprise infrastructure—whether cloud-based or on-prem—Zerve allows teams to orchestrate AI workflows securely and at scale.
“If you’ve tried out ‘vibe-coding’ agents and wished you could apply them to real enterprise AI and data workflows—now you can,” said Phily Hayes, CEO and co-founder of Zerve. “Zerve is hosted in your own environment, with your own SDLC policies, connected to your LLMs, data repositories, single sign-on, and the whole shebang, giving your team and AI agents a secure, productive environment in which to explore, build, test and deliver data and AI products in record time.”
Zerve’s visual canvas is a core differentiator. It allows humans and agents to collaborate on projects through a shared interface, where agents can propose plans, create and connect code blocks, provision compute, and execute jobs. The system supports multiple agents working in tandem on different parts of a workflow—a setup that mirrors how agile software teams operate.
In practice, this means that a user can prompt Zerve in natural language to solve a data problem, and the agents will generate a workflow plan. They execute this plan by creating canvases, coding solutions, managing infrastructure, and iterating when issues arise—just as a human team would. When failures occur, Zerve agents reevaluate and try alternate approaches, bringing an experimental and resilient mindset to the table.
The company’s latest release—Zerve 2.0—builds on its initial 2024 launch and introduces a suite of patent-pending features, including:
- The Fleet: A serverless computing engine that executes massively parallel workloads with a single command, ideal for large-scale LLM calls or data transformation jobs
- App Builder: A no-devops-required interface for turning data workflows into shareable applications, with native integration of AI agents that users can interact with via natural language
“As we advance our AI and data standardization initiatives, Zerve’s OS provides the robust foundation we need to scale with confidence and accelerate meaningful outcomes,” said Richard Springer, Director of Data at Cubic, a Zerve customer focused on software-defined vehicle platforms.
Zerve’s approach fits squarely within the rising trend of agentic AI—AI agents designed not just to assist, but to act. In contrast to standalone chatbots or copilots, agentic systems like Zerve orchestrate end-to-end workflows. The platform bridges the gap between experimentation and production, making it easier for enterprises to build repeatable, scalable AI systems.
Most importantly – solutions such as this are needed for AI agents to make the leap into complex environments where collaboration and integration are key.
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Aside from his role as CEO of TMC and chairman of ITEXPO #TECHSUPERSHOW Feb 10-12, 2026, Rich Tehrani is CEO of RT Advisors and a Registered Representative (investment banker) with and offering securities through Four Points Capital Partners LLC (Four Points) (Member FINRA/SIPC). He handles capital/debt raises as well as M&A. RT Advisors is not owned by Four Points.
The above is not an endorsement or recommendation to buy/sell any security or sector mentioned. No companies mentioned above are current or past clients of RT Advisors.
The views and opinions expressed above are those of the participants. While believed to be reliable, the information has not been independently verified for accuracy. Any broad, general statements made herein are provided for context only and should not be construed as exhaustive or universally applicable.
