The healthcare payer environment is going through a phase of unparalleled complexity. Health plans are no longer financial intermediaries; they become partners in their member health, data custodians and technology hubs. Nevertheless, the tension of legacy infrastructure systems can frequently introduce disparity between the need for agility and the operational implementation. This is where the interconnecting of Platform-as-a-Service (PaaS) and autonomous AI agents, Agentic PaaS, starts to transform the industry.
At STL Digital, we observe that the shift from static automation to dynamic, agentic workflows is not merely a trend but a necessary evolution for payers aiming to reduce administrative waste and improve member outcomes. To achieve this, organizations must look beyond simple cloud migration and embrace a holistic Enterprise Application Transformation Service that prepares their core systems to host and orchestrate these intelligent agents.
The Stagnation of Traditional Automation
For years, health plans have relied on Robotic Process Automation (RPA) to handle repetitive tasks. While effective for linear processes, RPA struggles with the nuance and variability inherent in healthcare data. A rigid bot can move a file from point A to point B, but it cannot interpret a complex clinical note or negotiate a prior authorization request based on ambiguous medical necessity guidelines.
Such limitation has resulted in a lot of players having a digital veneer of shiny front end apps that covers the clunky, manual-driven backend processes. True digital transformation in business requires systems that can think and act, not just repeat.
The urgency for this shift is reflected in the market’s investment priorities. According to a recent press release from Gartner, worldwide IT spending is expected to total $5.74 trillion in 2025, an increase of 9.3% from 2024, driven largely by spending on servers and infrastructure to support GenAI. This surge in investment underscores that the next phase of efficiency will not come from working harder, but from building the infrastructure to support smarter systems.
Defining Agentic PaaS in Healthcare
Agentic PaaS refers to a cloud-based platform environment designed specifically to build, deploy, and manage AI agents. These agents are goal-oriented unlike the traditional chatbots that wait till a user gives an input to respond. They are able to detect their environment, think in multifaceted situations, and perform actions in multi systems without having to be under constant human supervision.
- An Agentic PaaS layer would be a layer over the core claims and member data systems, in terms of a health plan. It is the orchestration engine wherein:
- Agents Collaborate: A “Benefits Agent” can engage with a “Clinical Policy Agent” to determine a claim, as a simulation of a claims adjuster and a nurse reviewer.
- Context is King: The agents have a long-term recollection of how members interact, and a discussion on a diabetes management plan in January would lead to a hospitalization authorization request in March.
- Algorithms of Governance: The reasoning models of the agents are hard-coded with compliance rules and thus ensure compliance with CMS regulations or state requirements.
Essential Health Plan Use Cases.
This technology is applied in the payer value chain. Health plans can address their most ingrained problems using the capabilities of robust Artificial Intelligence.
- Independent Utilization Management.
One such infamous point of friction is prior authorization. A clinical documentation (PDFs, faxes, EHR data) can be ingested into an agentic system and compared to the medical policies of the plan in seconds and a decision made or a particular missing information requested. This switches the process to more of a clinical check than an administrative challenge hence minimizing turn around times.
- Effective Care Coordination.
Rather than responding to a costly claim, the agents may examine real-time admission-discharge-transfer (ADT) feeds. In case of discharging a member, an agent will be able to automatically initiate a workflow to schedule a follow-up appointment, transport, and notify the care management team without the need of human involvement to address the task.
- Fraud, Waste, and Abuse (FWA) Detection.
Traditional FWA systems rely on retrospective pattern matching. AI agents can act as active monitors, analyzing claims streams in real-time to flag anomalies that deviate from provider peers or established coding behaviors, pausing payment for review before the money leaves the door.
The Role of Enterprise Application Transformation Service
Implementing Agentic PaaS is not as simple as purchasing a software license. It requires a fundamental modernization of the underlying IT estate. You cannot build high-speed AI agents on top of fragile, siloed legacy mainframes that cannot share data in real-time.
This is why a comprehensive Enterprise Application Transformation Service is the critical precursor to adopting agentic workflows. This service involves assessing current application portfolios, refactoring monolithic codebases into microservices, and ensuring that data is accessible via APIs that AI agents can call upon.
Without this transformation, AI agents are effectively blind and paralyzed. They may possess the intelligence to make a decision but lack the connectivity to execute it within the core administration system. Therefore, IT leaders must view modernization not as a housekeeping task, but as the foundation for AI readiness.
Navigating the Data and Security Landscape
For AI agents to function effectively, they require access to vast amounts of sensitive data. This brings security and data privacy to the forefront. Health plans must ensure that their cloud environments are secure by design.
The industry is already seeing the impact of these technologies. In a report by Deloitte regarding the 2025 global healthcare outlook, more than 40% of respondents said their organizations have already experienced a significant-to-moderate return on their investments in generative AI. As these returns become more evident, the pressure to adopt secure, scalable agentic frameworks will only increase.
Health plans need to implement “Data Operations” that ensure clean, governed data feeds for these agents. If the data regarding provider networks or drug formularies is outdated, the agent will make incorrect decisions. Continuous monitoring and “human-in-the-loop” protocols are essential during the early phases of deployment to validate agent accuracy.
Overcoming the Skills Gap
Moving to an Agentic PaaS model changes the requirements for the IT workforce. The demand shifts from traditional system maintenance to managing agent behavior, prompt engineering, and overseeing Cloud Services.
This shift is a strategic imperative for technology leaders. Bain & Company’s Technology Report highlights that generative AI is a top five priority for 85% of respondents, signaling a massive reallocation of focus toward these intelligent systems. Health plans must partner with specialized providers who can offer the requisite IT solutions and services to bridge this gap, providing both the technology and the talent to manage it.
The Strategic Roadmap for Payers
To successfully leverage Agentic PaaS, health plan CIOs and CTOs should follow a phased approach:
- Assessment and Modernization: Utilize an Enterprise Application Transformation Service to de-couple old systems and reveal the business logic via API.
- Pilot Agent Deployment: To test the agent efficacy, choose a low-risk, high-volume use case, e.g., replacement of member ID cards or simple claims status queries.
- Scale and Integrate: Agent capabilities should be extended to clinical areas such as care management, utilization review, with an intensive integration with the Cyber Security protocols to safeguard PHI.
- Continuous Governance: Have an AI centre of excellence to oversee agent performance, bias and drift so that the system is updated to reflect the changes in regulations.
Conclusion
The transition to Agentic PaaS represents a turning point for health plans. It offers the promise of a healthcare system that is not only more efficient but also more responsive and personalized for the member. However, this future is contingent upon a solid technological foundation. By prioritizing an Enterprise Application Transformation Service, payers can shed the constraints of legacy infrastructure and fully embrace the potential of AI for enterprise.
At STL Digital, we understand the intricacies of this journey. We are committed to helping health plans navigate the complexities of modernization, ensuring that technology serves as a catalyst for better care, lower costs, and a healthier world.
