You love LLMs and co-pilots? You’ll love AI Agents even more

Physician enthusiasm is growing for healthcare AI. Clinicians and providers love using LLMs; this is what we hear daily, and it is also reflected in the latest AMA survey about physicians’ sentiments around the use of AI. See the AMA’s latest full report on Augmented Intelligence Research here.

You like LLMs? You will love AI Agents.

AI agents are LLMs with agency, meaning they are built around (or on top of) models like GPT, Claude, or Gemini, but given tools, context, and goals that let them act autonomously.

They combine:

• An LLM for reasoning and understanding.

• A memory (so they can retain context and learn from interactions).

• Tools / actions (e.g., access to APIs, databases, browsers, schedulers, or internal systems).

• A goal or objective (e.g., “process claims,” “fetch a missing document from an EHR,” “estimate costs for patients,” or “create a 360-degree patient summary”).

Here is a concrete example: an LLM can explain how to submit a claim. An AI agent can actually do it—log into the portal, extract the right data, validate codes, and file it automatically.

Why do our customers need AI agents in healthcare operations and RCM?

For Efficiency: Healthcare operations are filled with repetitive, rule-based tasks, from eligibility checks to claims submission, that consume valuable staff hours. AI agents streamline these workflows, reducing friction and human error. By automating what’s predictable and repeatable, teams can operate with precision and consistency at scale.

For Time Savings: Administrative tasks consume full working days each week for many healthcare staff. AI agents give this time back by handling documentation, data extraction, data entry, and system-to-system coordination in seconds, not hours. The result is more time for patients, fewer backlogs, and less burnout across teams.

For Revenue Optimization: Billions are lost every year to avoidable claim denials, missing documentation, or slow payment cycles. AI agents continuously monitor, validate, and reconcile claims, identifying revenue leakage and recovering it in real time. They turn complexity into clarity, ensuring providers get paid faster and more accurately.

Is it about AI? No, it’s about solving real problems.

Processes: We start by understanding the process, not the technology. AI only works when it aligns with how healthcare teams actually function, from intake to billing. By deeply mapping existing processes, we can design interventions that truly improve outcomes, not just automate inefficiency.

Workflows: Healthcare workflows are interdependent; a delay in one step (like prior authorization) ripples across the entire revenue cycle. Our AI agents fit naturally into these workflows, connecting disconnected systems and creating seamless handoffs. The goal isn’t new tools; it’s smoother operations that feel invisible.

Impact: Our focus is measurable improvement: fewer denials, faster turnaround, and higher staff satisfaction. Impact isn’t theoretical; it’s operational, financial, and human. When agents work well, people notice less chaos and more calm in their day-to-day.

Data: Every agent interaction generates structured, contextual data that wasn’t visible before. This data helps organizations understand bottlenecks, compliance gaps, and trends, creating a feedback loop for continuous improvement. Data becomes not just a byproduct, but a strategic asset.

See below the AI agents we’ve built at XY.AI. They are agentic in nature, with orchestration in the backend. There are concrete examples of what AI agents can solve in healthcare operations:

How do we measure if they work?

With Value: We measure value by the outcomes that matter to our customers: reduced manual effort, lower error rates, and faster resolution times. Each agent is benchmarked against key performance indicators defined collaboratively with the client. Value is demonstrated, not promised.

With ROI: Return on investment is tracked in real numbers—hours saved, dollars recovered, and administrative cost reductions. We often see ROI realized within weeks, not months, as recovered revenue and efficiency gains outpace implementation costs. AI is only meaningful if it improves the financial health of the organization.

With Impact: Beyond efficiency and cost, we measure human impact—reduced burnout, improved morale, and a sense of regained control. When AI agents take over the soul-crushing tasks, people can refocus on purpose: caring for patients and running sustainable organizations. The impact is systemic, not incremental.

Why a Platform vs. Point Solution?

For Orchestration: Healthcare operations are interconnected; fixing one bottleneck in isolation often shifts the problem elsewhere. A platform approach allows multiple agents to coordinate across functions, ensuring end-to-end flow and oversight. It’s the difference between adding tools and building an intelligent, secure, and auditable ecosystem.

With Upstream/Downstream Integration: Revenue cycle efficiency depends on upstream accuracy and downstream visibility. A platform connects these layers—linking intake, coding, billing, and payment—so data and insights flow seamlessly. This holistic approach eliminates silos and reduces the friction that point solutions can’t address.

With Continuous Learning: A true platform learns from every interaction across customers, tasks, and data streams. Agents continuously improve accuracy, compliance, and contextual understanding over time. This turns automation into evolution: your operations get smarter the longer you use the system.

When AI serves people, everyone wins — patients, providers, and the business of care itself. Exciting times ahead!

Originally published on LinkedIn, 14 November 2025.