Demystifying Agentic AI: How AI Agents Can Change Healthcare Efficiency Now

Artificial Intelligence (AI) is more than just another tool. As our understanding of its capabilities evolves, AI is being leveraged in new ways through Agentic AI. This system doesn't just respond to commands but autonomously orchestrates actions, makes decisions, and adapts to changing information. This is crucial in healthcare, where operational inefficiencies and rising costs are major concerns, especially in payer-provider collaboration and administrative workflows.

With AI dominating headlines, you may wonder: What’s different about Agentic AI? And why is it potentially the right solution for addressing these challenges now?

What is Agentic AI?

Agentic AI refers to an intelligent architecture designed to create AI Agents—autonomous systems that not only generate responses but also actively execute workflows, manage data, and plan tasks to achieve specific goals. While traditional AI is often designed for specific tasks, agents in Agentic AI act independently, operating across processes to reduce manual workload and alleviate data fragmentation.

For example, in healthcare:

• Claims Processing: AI agents autonomously review claims, verify documentation, and resolve discrepancies, reducing approval times by 30%.
• Care Coordination: AI agents synthesize fragmented data, ensuring high-risk patients receive timely interventions and reducing unnecessary readmissions.
• Authorization Requests: Agents analyze eligibility, flag bottlenecks, and expedite prior authorizations, reducing manual review times by 40%.

In healthcare, AI agents can enhance—but not completely replace—automation for complex tasks. These agents synthesize information, manage workflow bottlenecks, and adapt dynamically to the context of ongoing processes like care coordination or claims management, making them invaluable to improving operational efficiency.

How Agentic AI Works

Let’s break down how these AI agents operate and why they’re such a game-changer:

• Agent as the Orchestrator: The agent autonomously interprets a request and orchestrates the necessary actions. It doesn’t just provide a recommendation; it manages processes independently. For example, in care coordination, the agent proactively gathers data from multiple sources, identifies gaps in care, and autonomously schedules follow-ups, reducing the need for manual intervention and ensuring continuity of care for high-risk patients.

• Memory: Traditional AI typically works in isolated interactions without memory. However, Agentic AI retains and recalls critical information across interactions, like patient history or care preferences. This means an agent managing chronic care coordination can recall the patient’s last visit details and automatically apply that context to future interventions, ensuring seamless care.

• Tools: Agents connect to APIs, databases, and real-time systems, acting as intermediaries between disparate platforms. These are referred to as Tools. For instance, an agent might automatically access medical records and financial databases to gather all relevant information, simplifying care plan approvals and reducing the need for manual input.

• Planning: Agentic AI excels at managing complex, multistage workflows. In care management, agents can break down tasks into manageable steps. For example, an agent managing a patient’s post-discharge care proactively coordinates with clinicians, schedules follow-up appointments, and tracks patient outcomes to ensure care continuity. This reduces administrative burden, ensures timely interventions, and improves patient outcomes.

How Agentic AI Utilizes Large Language Models (LLMs)

LLMs enhance Agentic AI’s ability to understand complex requests and generate accurate responses. Here’s how LLMs integrate with the core components:

• Orchestrator: LLMs process vast amounts of unstructured data, such as claim notes or patient histories. The LLM interprets this input, and the agent executes tasks autonomously, such as deciding the next best action in claims processing.
• Memory: With memory integration, LLMs retain critical context over time. For example, when tracking ongoing patient care, the LLM helps recall prior care adjustments, ensuring the agent makes informed decisions.
• Tools: LLMs enable agents to interpret data from multiple tools. For example, the LLM guides interpretation to ensure coherent decision-making across systems in managing prior authorizations.
• Planning: LLMs assist agents in planning multistage workflows. The agent predicts what tools are needed and anticipates next steps in tasks like managing care plans, coordinating between teams, and reducing hospitalizations.

LLMs, like GPT models, enhance cognitive capabilities, enabling agents to process context and data. Depending on organizational needs, these models can be fine-tuned for healthcare-specific tasks, hosted privately for greater control, or customized using open-source versions. Depending on deployment needs, LLMs can be:

• Public base models that are fine-tuned for healthcare-specific tasks.
• Private LLMs can be hosted in secure, private cloud environments for greater control.
• Open-source LLMs that can be customized and run entirely on your infrastructure.

This versatility allows payers and providers to balance performance, privacy, and compliance.

Agentic AI and Multi-Agent Systems

A compelling application of Agentic AI is multi-agent systems, where multiple specialized agents work together on separate but interconnected tasks. For example, in care management, one agent might handle patient data synthesis while another automates care plan updates. They communicate to ensure that tasks are aligned, creating an integrated system that operates across multiple workflows simultaneously. It is fascinating that each agent could use different technologies for its purpose, allowing you to apply best-in-class emerging technologies for the right task.

By deploying multi-agent systems, healthcare organizations can ensure that different parts of their operations are running efficiently without bottlenecks or information silos.

Practical Examples of Agentic AI in Action

1. Claims Processing

AI agents assist in reviewing claims, verifying documentation, identifying discrepancies, and flagging issues. AI agents? can send non-approvals or denials to humans for further review, assist clinical teams in their review of the claim ensuring fairness and regulatory compliance. These agents streamline approval processes using real-time data and predictive analytics, cutting review times by 30%, reducing administrative overhead, and minimizing bottlenecks.

Agent’s Role: The agent uses memory to track claim history, accesses APIs to verify eligibility, and plans necessary steps to expedite approvals, ensuring smoother claim workflows.

2. Care Coordination

Care teams often struggle with fragmented information across different systems, which can result in gaps in patient management. AI agents work by gathering data from electronic health records, care management platforms, and scheduling systems, allowing them to streamline workflows efficiently.

Agent’s Role: The agent knows how to invoke source-specific agents that retrieve various data points, synthesizing them to give a full picture. It predicts which high-risk patients need immediate intervention and automates collaboration across care teams. This reduces preventable readmissions and improves patient outcomes and overall efficiency.

3. Authorization Requests

AI agents handle prior authorizations by autonomously evaluating resource utilization, eligibility criteria, and required documentation. They flag potential bottlenecks in real-time, reducing the review process by 40% while providing transparency for both payers and providers.

Agent’s Role: The agent accesses patient and billing data, applies rules to check authorization eligibility, and executes the workflow autonomously without requiring manual intervention.

4. Data Reconciliation

For financial and operations teams, reconciling payment data from claims to outgoing provider payments can be a labor-intensive process prone to errors. AI agents automate this by cross-referencing claims data, provider records, and financial transactions, ensuring that payments match claims accurately. This reduces manual reconciliation work by 25%, improves accuracy, and helps detect any discrepancies.

Agent’s Role: The agent accesses financial databases, claim systems, and provider records, ensuring any inconsistencies in payment amounts or timing are automatically flagged and quickly resolved.

How AI Agents Are Different From Traditional AI

Some may wonder: Aren’t we already seeing AI handle these tasks? The answer lies in how Agentic AI components work together—creating a cohesive, proactive system rather than reactive automation.

1. Beyond RPA Bots: While RPA bots are rules-based and typically limited to predefined tasks, AI agents adapt dynamically. Agents can change strategies mid-process, interact with evolving data, and operate independently with little human input. For example, An RPA bot might be programmed to log into a system and extract patient appointment data at the end of each day. In contrast, an AI agent could analyze this data in real time, adjusting schedules dynamically based on patient cancellations, emergency bookings, or overbookings. The AI agent adapts and adjusts workflows mid-process to optimize appointment schedules without human intervention.
2. Not Just a Chatbot: AI agents aren’t simple conversational bots. While chatbots offer basic interactions, agents can manage entire workflows. They orchestrate data synthesis, decision-making, and execution across complex healthcare processes. For example, a hospital chatbot might handle basic patient queries about operating hours or appointment statuses. However, an AI agent could manage an entire patient referral process. It could analyze the patient's medical history, schedule appointments with the right specialist, and follow up automatically post-visit to coordinate any required lab tests or further consultations, all based on evolving patient data and needs.
3. Multistage Workflows: Traditional AI typically focuses on one task at a time, but AI agents engage in planning—breaking down complex problems into smaller steps and managing each part of the process. They ensure continuity, whether tracking a patient’s care from hospital admission to post-discharge or processing a claim from submission to approval. For example, in traditional AI, a single step, such as reading a lab result, might be automated. However, an AI agent can manage a patient’s entire care journey. For example, from hospital admission, where it gathers patient data, to discharge planning, where it synthesizes input from multiple healthcare professionals, ensuring follow-up care is coordinated and documented. It ensures the entire process flows seamlessly, from data intake to follow-up visits, with every step adapted to patient changes in real-time.

Agentic AI Industry Momentum & Productive Edge AI Accelerators

Recent innovations from major tech players highlight the growing role of AI agents in healthcare. Google's latest insights emphasize the importance of integrating fragmented data for accurate, scalable AI applications in health operations. This aligns with Productive Edge's AI Accelerators, which streamline workflows by dynamically synthesizing real-time data, offering seamless integration with existing systems to drive efficiency. As healthcare organizations face rising costs, AI agents provide a timely, scalable solution.

• Microsoft recently introduced new autonomous agents that enhance team scalability by automating complex, multistep workflows. These agents work dynamically, scaling tasks across business operations with minimal human input.
• Salesforce is developing Agentforce , which integrates AI agents into customer relationship management (CRM) systems. These agents autonomously handle workflows like managing client data, automating routine tasks, and enabling deeper customer interactions through AI-driven decision-making.
• Crew AI and Langchain focus on leveraging agentic frameworks that integrate LLMs (Large Language Models) with external tools to enable AI agents to act autonomously in various business environments.

Productive Edge’s Accelerators build upon these innovations. By integrating Agentic AI frameworks, P/E’s accelerators are designed to deliver immediate value in healthcare by bringing AI agents to life. For example, our accelerators automate claims processing, care coordination, and member engagement tasks—cutting operational costs, improving workflow efficiency, and dynamically adapting to evolving data. Our approach leverages pre-defined workflows, real-time data tools, and dynamic decision-making, helping healthcare payers and providers harness the potential of AI without the need for complex system overhauls.

In this context, P/E’s AI Agent Accelerators act as a bridge, allowing healthcare organizations to capitalize on the latest advancements from significant tech players while delivering measurable improvements today.

Why Healthcare Leaders Should Embrace Agentic AI Today

With payers and providers facing increased pressures—from administrative inefficiencies to rising operational costs—Agentic AI offers a clear path forward. These agents can reduce claims processing times, streamline care coordination, and provide real-time decision support, improving patient outcomes and operational efficiency.

The real advantage of Agentic AI lies in its ability to integrate with existing systems, filling in the gaps where today’s platforms, like Epic, might not yet have full AI capabilities. Instead of waiting for future innovations, healthcare organizations can deploy these agents now to capture immediate benefits.

The Future of Healthcare with AI Agents Agentic AI is more than a trend—it’s the future of healthcare efficiency. By combining memory, tools, planning, and autonomy, these agents transform fragmented workflows into coordinated, proactive processes that drive immediate impact. Now is the time for payers and providers to act—AI agents are ready to deliver measurable results today.