Why Investors are Betting Big on Agentic AI for Health Systems

Healthcare is entering a new phase of AI adoption, moving beyond predictive models toward agentic artificial intelligence (AI) systems capable of autonomous action: scheduling patients, adjudicating claims, coordinating care, and inserting themselves into clinical workflows. For CIOs, CMIOs, and tech leaders in health systems, this shift demands not only model maturity but infrastructure readiness, governance rigor, and human oversight design.

As funding accelerates and partnerships multiply, early adopters are testing the boundaries of what these systems can safely automate. Below is an excerpt from an article by David Raths, Healthcare Innovation Contributing Senior Editor, that frames the investment trajectory and early deployment strategies:

As reported by David Raths in “Venture Capitalists See Big Opportunity for Agentic AI in Healthcare” on Healthcare Innovation:

“The shift to agentic AI in healthcare is accelerating, with each day bringing new partnership announcements and venture capital deals.

On Sept. 30, San Francisco-based Assort Health, which says it has created a patient experience platform powered by specialty-specific agentic AI, closed a $76 million Series B financing round. In another example, Duke Health said it would co-develop and test agentic AI products with Trase Systems, with the first phase of development beginning at the Duke Heart Center.

Recently Healthcare Innovation interviewed Fawad Butt, founder and CEO of Penguin Ai and former chief data officer of UnitedHealthcare, Kaiser Permanente and Optum, about the transition taking place to the new world of agentic AI.

His Palo Alto, Calif.-based company has pulled in $29.7 million in venture funding and says its flagship platform combines task-specific small language models (SLMs), digital workers and agents, with a healthcare-specific AI platform to streamline processes such as prior authorizations, claims processing, medical records summarization, and appeals management.

‘We built our own small language models for prior auth, risk adjustment, and claims adjudication, and then we give you our agents out of the box,’ Butt said. ‘That’s what a platform is supposed to do. It’s supposed to give you what you need so you can get to ROI in 90 to 120 days.’

A blog post from the law firm of Morgan Lewis noted that in the third quarter of 2025, $17.4 billion was invested in applied AI, marking a 47% increase year over year. ‘Projections suggest that spending on agentic AI could reach $155 billion by 2030. The focus has shifted from developing large language models (LLMs) to integrating AI into workflows. Investors are prioritizing startups that demonstrate traction in enterprise adoption, with deal terms emphasizing integration over innovation.’”

Continue reading “Venture Capitalists See Big Opportunity for Agentic AI in Healthcare” by David Raths on Healthcare Innovation. Read the full article.

Why It Matters to You

For TechEDGE readers in regulated, data-rich domains, healthcare’s pivot to agentic AI offers an instructive model—one that faces extreme constraints, high stakes, and stringent oversight. If these systems can safely and reliably automate workflows in clinical environments, the implications ripple into finance, logistics, infrastructure, and industrial operations.

But agentic AI isn’t plug-and-play. Success depends on embedding governance, audit trails, fail-safe control loops, and contextual alignment with human workflows. The explosion of funding means many proposals will surface, but only those that integrate action with reliability and compliance will scale.

Next Steps

• CTO/AI Lead: Identify a noncritical workflow (e.g., scheduling, claims routing) to pilot an agentic module with human review. 
• Clinical/Operational Teams: Map workflow boundaries and failure modes in advance—define “stop conditions” for automated agents. 
• Governance/Risk/Legal: Build auditability, traceability, and fallback protocols for agentic actions in regulated settings. 
• IT/Infrastructure: Ensure real-time context data, such as EHR, scheduling systems, and identity, is integrated and can support decision loops. 
• Strategy/Investment: Prioritize agentic AI investments with demonstrated domain fit and pilot proof-of-concept, not just model novelty.