Most leaders want to understand what role AI should play in healthcare engagement. While many are excited about the technology, there are still an equal number of leaders who are cautious.
Many health plan leaders I speak to are trying to determine where AI actually moves the needle and makes a difference in the industries they serve. The real distinction isn’t between models - but it is between two fundamentally different system designs: chatbots and autonomous agents.
Most leaders want to understand what role AI should play in healthcare engagement. While many are excited about the technology, there are still an equal number of leaders who are cautious. I share that mix of hope and concern.
During the past year, what has become clear to me is that the answer depends less on the model and more on the design of the system around it. That is where the distinction between chatbots and autonomous agents becomes important.
A lot of what the industry calls AI today still follows the chatbot pattern. The interfaces are better and the language models are more capable, but the underlying design is the same. A chatbot handles the task and the moment in front of it. It waits for a question, responds, and then resets. That approach works for simple transactions. It does not carry the larger responsibility of understanding the member’s situation or emotion or acting when something important should happen. Those gaps show up quickly in healthcare, where timing, context, and trust matter.
Autonomous agents are built to take responsibility for defined workflows. They maintain a persistent understanding of the member, monitor when action is needed, try alternative paths when the first attempt fails, and communicate as part of completing a task—not just answering questions. This shift only works with strong guardrails such as observability, clear decision boundaries, and human oversight.
The Foundation is Coherent Data and Dynamic Cohorts
Plans lack consistency. Claims, care management, EHR feeds, and SDOH sources rarely speak the same language. Agents don’t need a massive warehouse of data, but they do need a stable way to interpret signals. A good agent knows when not to act because information is incomplete or contradictory.
Once an agent understands individuals, it must also understand populations. Static segmentation can’t keep up with fast-changing behaviors. Dynamic cohorts allow agents to recognize real-time patterns (such as who responds in the evening, who consistently stalls at step two, who is motivated but blocked by logistics). These cohorts must remain explainable and governed so leaders know why someone was included and how behaviors shift over time.
Learning With Boundaries
Agents improve as they observe outcomes, but learning in healthcare must be controlled. Adjusting timing or channel sequence is appropriate. Adjusting clinical guidance is not. Experiments require guardrails, review, and a record of what changed. When that structure exists, automation becomes more accurate, more efficient, and safer.
Human Oversight -Above and In the Loop
Agents aren’t replacing teams, they’re scaling what teams can’t do. They are taking on repetitive, follow-up-heavy work so humans can step in for nuance and judgment. Oversight ensures the system stays aligned with policy, regulatory requirements, and clinical guardrails.
Organizations don’t need to replace existing systems to deploy agents. They can begin in narrow, well-governed workflows, prove value, and expand as confidence grows. This incremental path is far more realistic than a full-stack transformation.
Proof Today: What Voice Agents Are Already Delivering
RadiantGraph’s autonomous Voice Agents show the impact of this architecture in real operations. Across thousands of members:
75 hours saved per 1,000 members contacted
15.8× increase in enrollment among high-risk groups
Consistent performance across timing, retry logic, and next-step clarification
These results are not about a better script, they come from agents that understand members, operate under defined rules, and improve through governed learning.
Agent Use Cases
Going into 2026 Autonomous agents will increasingly support:
Gap closure workflows: automatic detection, outreach, and documentation guidance
Enrollment journeys: multi-step follow-up, requirement clarification, and deadline management
Benefit navigation: nudges personalized by SDOH, preferences, and behavioral patterns
Care coordination: triaging simple tasks so humans can focus on clinical complexity
The takeaway: AI already has a place in healthcare engagement. Chatbots will remain tools for single interactions. But agents (when designed with the right balance of autonomy, supervision, and data coherence) can take responsibility for ongoing engagement in a way chatbots never will.
The organizations that succeed will treat autonomy as a discipline. They’ll build systems that understand members, respect boundaries, and improve in ways leadership can observe and guide. Done right, autonomous agents make engagement more consistent, more efficient, and more supportive across a member’s entire journey.