Agentic AI Company H Launches Cloud-Based Web Agent

These agents can automate complex, repetitive workflows while remaining under human oversight.

H, a provider of agentic artificial intelligence, has launched Runner H, a cloud-based web agent (Source: jcomp/Freepik)

H, a provider of agentic artificial intelligence, has launched Runner H, a cloud-based web agent. The company has announced the beta release of Runner H 0.1, taking a step towards artificial superintelligence.

H leverages its proprietary foundation and action models to deliver advanced agents capable of reasoning, planning, and execution. These agents automate complex, repetitive workflows while remaining under human oversight. Runner H can integrate with multi-system and legacy solutions in large enterprises while offering entrepreneurs a virtual team of agents.

"We are thrilled to bring agentic AI to market with Runner H," said Charles Kantor, chief executive officer of H. "This is about empowering people to harness AI like never before. Runner H allows users to automate workflows, streamline tasks, and own the web."

Runner H can undertake the following tasks:

  • Robotic Process Automation: Runner H can enable companies to automate their recruitment process, from posting job descriptions to aggregating resumes and sending follow-ups. It offers insights from LinkedIn and other platforms, updates CRMs, and handles onboarding. 

  • Quality Assurance: The web agent adapts to user interface changes, automates repetitive testing processes, and navigates workflows, such as validating page availability, simulating real-user actions, and enabling compatibility across payment methods.

  • Business Process Outsourcing: Runner H can automate tedious tasks like fetching insurance plans, compiling notes, analysing and submitting claims, and tracking reimbursements, removing outsourcing dependencies.

H's compact models (large language models — Runner’s brain, and vision language models — Runner's eye, both 2B parameters) have surpassed the performance of other alternatives, the company said, demonstrating the potential of smaller, more focused models to outperform generalist models.

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