Navigating the Blurred Lines Between Agents and AI Tools in Healthcare
Cutting Through the AI Buzzwords to Make Better Build-or-Buy Decisions
The MRI Appointment That Scheduled Itself
Imagine a future where a patient contacts your clinic's chat assistant late at night, seeking an MRI appointment. By morning, an AI-driven system had autonomously verified her insurance coverage, selected an MRI slot compliant with both her deductible and the radiologist’s availability, and electronically forwarded the prior authorization paperwork to her primary care physician. At face value, this might seem like a straightforward use of technology—yet beneath the surface, multiple AI components, each potentially classified as "agents," communicated seamlessly. Technology specs like Microsoft's Copilot Studio supported Model Context Protocol (MCP) and the Agent-to-Agent (A2A) specification made this sophisticated coordination possible, highlighting how modern advancements increasingly blur the lines between AI tools and agents.
Distinguishing Agents from Tools: A First-Principles Approach
With the surge in AI Agent capabilities and Agent tool orchestration, terminology confusion has emerged. To bring clarity, I propose evaluating AI components through three foundational criteria:
Autonomy: Can the AI independently decide how to achieve a goal?
Memory (Statefulness): Does the AI retain context across multiple steps or interactions?
Tool Use: Is it capable of autonomously selecting and orchestrating other functions or resources?
Components meeting all these criteria qualify as agents. If any criterion is unmet, we're discussing a tool, regardless of branding. Consider the analogy of an orchestra: a conductor represents an agent, orchestrating actions dynamically, while instruments represent tools—powerful yet inert without direction.
Importantly, this isn’t a binary choice. In many real-world applications—especially when deploying your own agents—the answer is often both. Agents frequently leverage one or many tools to complete a goal. The classification depends less on whether tools or agents are present, and more on how orchestration, autonomy, and memory are handled.
How MCP and A2A Protocols Complicate the Picture
Traditionally, the distinction was straightforward: agents managed orchestration, state and workflow, while tools performed discrete, callable functions. However, recent protocols like MCP and A2A have significantly complicated this clarity. MCP allows AI models to dynamically invoke any structured API securely, transforming basic functionalities into seemingly agent-like capabilities. Concurrently, A2A lets AI agents discover and invoke each other’s capabilities as if they were mere tools, leading to recursive layers of autonomy and orchestration. Thus, what might previously have been labeled a tool can now, with minimal modification, present as an agent, depending on the consumer's perspective.
Leveraging Semantic Kernel to Bridge Tools and Agents
Microsoft’s Semantic Kernel (SK) exemplifies a framework uniquely capable of supporting both AI patterns effectively. Using SK, developers can rapidly create simple tools—single-purpose semantic functions—and equally effortlessly transition these into sophisticated, memory-aware, multi-step agents. For instance, an SK-based function might simply generate ICD-10 codes from clinical descriptions—a clear tool. However, when integrated with SK’s planning capabilities, the same framework can autonomously orchestrate multiple tools to manage complex administrative workflows, such as insurance verification and appointment scheduling.
Real-world Healthcare Applications: Administrative and Clinical
Today, healthcare executives already see the tangible benefits of agentic AI in administrative scenarios. AI-driven systems can independently verify patient insurance coverage, dynamically manage scheduling constraints, and automate communication, substantially reducing administrative overhead and decreasing errors associated with manual processes.
In clinical settings, the full agentic potential remains emerging but is incredibly promising. Imagine clinical decision-support agents that proactively pull patient data via standardized FHIR interfaces, query the latest medical guidelines, suggest differential diagnoses, and draft treatment plans for clinician review. Early-stage implementations are already demonstrating significant potential, though regulatory safeguards ensure human oversight remains central.
Strategic Decision-making: Understanding the Relationship
Rather than choosing between implementing AI as a tool or as an agent, recognize that an agent may use one or many tools as part of its operation. Especially in enterprise settings, deploying your own agent often means deploying both. The decision point isn’t agent vs. tool; it’s understanding how they relate, and when one needs to orchestrate the other.
Straightforward, repetitive tasks with defined parameters are typically best addressed by tools, providing rapid deployment, predictable outcomes, and straightforward governance. These are ideal for accelerating workflows where the logic is fixed and the scope is narrow.
Conversely, tasks involving complex decision trees, multiple data sources, dynamic interactions, or conversational interfaces often benefit from agent-based solutions. These require comprehensive governance frameworks, robust monitoring, escalation protocols, and human oversight mechanisms to ensure compliance, security, and patient safety.
Governance Imperatives in the Multi-Agent Era
As healthcare increasingly adopts agentic AI, rigorous governance becomes non-negotiable. Executives must prioritize implementing robust access controls, comprehensive audit trails, clearly defined escalation triggers to human oversight, and version control practices for all agent-based deployments. Governance strategies should directly map to existing regulatory frameworks, including HIPAA compliance, CMS guidelines, and broader organizational risk management strategies.
Final Thoughts: Clarity Drives Responsible Innovation
The next time the term "agent" crosses your desk, apply the litmus test of autonomy, memory, and tool-use. Understand clearly how MCP and A2A protocols may influence the functional categorization of AI components. Precision in terminology and implementation strategy not only ensures wise investments but also helps healthcare organizations responsibly navigate AI’s transformative capabilities, ultimately delivering tangible improvements in efficiency, patient outcomes, and clinician experience.