In the realm of artificial intelligence, many agents are capable of verbal communication. However, the question of trust is seldom addressed.
The urgency in healthcare today is palpable - teams are drowning in time-consuming tasks that delay patient care. Clinicians are overworked, call centers are overflowing, and patients are left waiting for answers. But fear not, AI agents can be a game-changer by filling the gap, extending the reach of medical and administrative staff, and reducing stress for both healthcare professionals and patients.
However, trust isn't won with a friendly tone or intelligent chat, it's all about the engineering. Healthcare leaders, responsible for their patients and communities, are hesitant to deploy AI at scale because they worry about the technology's capabilities. Some startups claim agentic AI that automates mundane tasks from scheduling appointments all the way up to high-touch patient communication and care. But the harsh reality is, many of these agents lack the safety needed for healthcare.
The problem is, it's simple to create an AI voice powered by a large language model (LLM), add a compassionate tone, and script a convincing conversation. Countless platforms promise their agents in every industry, but they all behave the same - prone to hallucinations, unable to verify critical facts, and lacking accountability mechanisms.
Healthcare isn't like retail or hospitality, foundational models aren't specific to clinical protocols, payer policies, or regulatory standards. Without proper safeguards, these chatty AI agents can wander into hallucinatory territory, answering questions they shouldn't, inventing facts, or failing to recognize when they need a human's help. The consequences aren't just confusing - they can interfere with care, confuse patients, and lead to costly human rework.
To operate safely in healthcare, AI agents need more than just an autonomous voice on the other end of the line. They must operate within a system engineered specifically for control, context, and accountability. Based on my experience building these systems, here's what that looks like in practice:
Response Control Can Eliminate Hallucinations
AI agents in healthcare can't just generate plausible answers. They need to deliver accurate ones every time, without fail. This requires a controllable "action space" - a mechanism for understanding and facilitating natural conversation while ensuring each possible response is bounded by predefined, approved logic.
With response control parameters built in, agents can only reference verified protocols, predefined operating procedures, and regulatory standards. The model's creativity is harnessed to guide interactions rather than improvise facts. By designing this risk out on the ground floor, the risk of hallucination vanishes.
Specialized Knowledge Graphs Ensure Trusted Exchanges
The context of every healthcare conversation is deeply personal. Two people with type 2 diabetes might live in the same neighborhood and fit the same risk profile, but their eligibility for a specific medication varies based on medical history, doctors' treatment guidelines, insurance plan, and formulary rules.
AI agents need access to this context and the ability to reason with it in real time. A specialized knowledge graph provides that capability. It's a structured way of representing information from multiple trusted sources that allows agents to validate what they hear and ensure the information they give back is both accurate and personalized.
Robust Review Systems Can Evaluate Accuracy
After a patient hangs up with an AI agent, the work's just beginning. Healthcare organizations need to ensure that the agent provided accurate information, understood and documented the interaction, and determined whether follow-up was required. That's where automated post-processing systems come in. A robust review system should evaluate each conversation with the same level of accuracy as a human supervisor. If something isn't right, the agent should escalate to a human, but if everything checks out, the task can be checked off the to-do list with confidence.
Beyond these three foundational elements, every agentic AI infrastructure needs a robust security and compliance framework that protects patient data and ensures agents operate within regulated bounds. This framework includes strict adherence to common industry standards like SOC 2 and HIPAA, as well as processes for bias testing, protected health information redaction, and data retention.
These security safeguards aren't just compliance boxes; they form the backbone of a trustworthy system capable of managing every interaction at a level patients and providers expect. The healthcare industry doesn't need more AI hype, it needs reliable, trustworthy AI infrastructure. And in the case of agentic AI, trust won't be earned; it will be engineered.
Artificial-intelligence (AI) agents, to be effective in healthcare, require careful engineering. Response control can eliminate hallucinations by ensuring that AI agents deliver accurate responses every time, based on predefined, approved logic.
Specialized knowledge graphs ensure trusted exchanges by providing a structured way for agents to reason with contextual information from multiple trusted sources in real-time. Furthermore, robust review systems can evaluate the accuracy of each conversation, escalating to a human when necessary. These foundational elements, along with a secure and compliant infrastructure, are crucial in engineering trustworthy AI agents for healthcare.
