Harris & Harris Transitions to Agentic AI: What It Is and Why It Matters for Our Healthcare Partners

May 6, 2026AI in Revenue Cycle Management0 comments

Harris & Harris Transitions to Agentic AI: What It Is and Why It Matters for Our Healthcare Partners

Originally published by Frost-Arnett, which Harris & Harris acquired on May 4, 2026.

For more than a century, Frost-Arnett has evolved alongside the healthcare industry, adapting our services as technology, regulations, and patient expectations change. In recent years, we have incorporated machine learning, robotic process automation, and artificial intelligence to improve efficiency, accuracy, and outcomes for our client partners.

Today, we are taking the next step forward.

Harris & Harris is actively transitioning both current and future AI use cases toward Agentic AI, a more advanced approach that allows intelligent systems not just to assist with tasks, but to reason, act, and adapt toward defined outcomes, all while maintaining the compliance, oversight, and patient respect that healthcare demands.

So, what is Agentic AI, and how does it differ from the AI technologies healthcare organizations have used in the past?

Understanding Agentic AI

Agentic AI refers to artificial intelligence systems designed to operate as goal-driven agents. Rather than waiting for step-by-step instructions, these systems can:

  • Understand a defined objective
  • Break that objective into actionable steps
  • Make informed decisions along the way
  • Take appropriate actions across systems
  • Learn from outcomes and continuously improve

In short, Agentic AI does not simply respond. It works toward a result.

For Harris & Harris, this means AI that supports smarter workflows, faster resolution, and better experiences for both healthcare providers and patients.

How AI Has Evolved

To understand the significance of Agentic AI, it helps to look at how AI capabilities have progressed over time.

Rule-Based AI

Early systems followed rigid logic:

  • If X happens, do Y
  • No learning or flexibility
  • Fully dependent on predefined rules

Example: scripted patient chat responses

Machine Learning

Machine learning introduced data-driven insights:

  • Models learned from historical patterns
  • Useful for predictions and classification
  • Still task-specific and largely reactive

Example: propensity-to-pay modeling or claim workflow prioritization

Generative AI

Generative AI brought flexibility and language understanding:

  • Generates text, summaries, and content
  • Interacts using natural language
  • Still requires human prompting

Example: summarizing claim notes or drafting denial appeals

What Makes Agentic AI Different

Agentic AI builds on generative models but adds autonomy, reasoning, and action.

 

This evolution enables AI systems to operate more like digital teammates, not just tools.

Core Capabilities of Agentic AI

Agentic AI systems typically share several defining characteristics.

Goal-Oriented Planning

The system understands what needs to be achieved and determines how to get there.

Example: resolving an insurance claim by identifying payer issues, gathering documentation, executing follow-ups, and escalating when needed.

Autonomous Decision-Making

The AI evaluates conditions and determines the next steps without manual intervention at every stage.

Action Across Systems

Agentic AI can interact directly with payer portals, internal databases, APIs, and workflow tools.

It does not just recommend actions. It carries them out.

Feedback and Continuous Improvement

Results are evaluated, and strategies adjust over time to improve efficiency, compliance, and outcomes.

Real-World Applications at Harris & Harris

Agentic AI enables use cases such as:

  • Insurance follow-up agents that analyze claim data, identify issues, and execute next steps
  • Workflow agents that coordinate tasks across multiple systems
  • Patient-facing agents that deliver compliant, respectful, and responsive experiences
  • Operational agents that reduce manual effort while increasing consistency and accuracy

These capabilities allow Harris & Harris to scale expertise without sacrificing accountability or patient experience.

Why Agentic AI Matters in Healthcare

Healthcare is complex, regulated, and constantly evolving. Agentic AI allows us to respond to that reality by:

  • Improving productivity through end-to-end task execution
  • Enhancing consistency and compliance across workflows
  • Supporting better patient experiences through faster, clearer resolution
  • Allowing human teams to focus on oversight, strategy, and relationships

Agentic AI is not about replacing people. It is about augmenting expertise, strengthening controls, and delivering better results at scale.

Looking Ahead

As healthcare continues to change, Agentic AI allows Harris & Harris to adapt continuously, supporting operational excellence while remaining patient-focused and compliance-driven workflows.

From back-end solutions to patient-facing interactions, Agentic AI positions Harris & Harris to deliver stronger outcomes for our healthcare partners and a better experience for the patients they serve.

To learn more about how Harris & Harris is leading the way with Agentic AI, schedule a conversation with our team today or call us at 1-844-729-5763.

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