How agentic AI and Microsoft Copilot are transforming healthcare and life sciences through intelligent, autonomous AI agents.
Agentic AI
Artificial intelligence is rapidly reshaping Healthcare and Life Sciences, but the next major evolution goes beyond automation toward agentic AI systems capable of reasoning, collaboration, and autonomous action. At the forefront of this shift is Sunil Kumar Gingade Krishnamurthy, Senior Cloud Solution Architect at Microsoft, whose work focuses on building secure, interoperable, and scalable agentic AI journeys using Microsoft Copilot and Azure AI.
In this interview, Sunil shares insights into how agentic AI is redefining clinical workflows, research operations, and enterprise healthcare systems.
Q1: Your recent work focuses on agentic AI in Healthcare and Life Sciences. Can you explain what that means?
Sunil Kumar:
Agentic AI refers to intelligent systems that can reason, take action, collaborate with humans, and automate full workflows. In modern Healthcare and Life Sciences, these agents support areas such as clinical documentation, research analytics, regulatory workflows, and patient operations.
What makes today’s systems particularly powerful is the shift toward advanced Large Language Models (LLMs) that enable multi-agent orchestration. Multiple AI agents can coordinate tasks, hand off responsibilities, and collaboratively resolve complex healthcare problems. This represents a major leap forward compared to traditional single-model AI systems.
Q2: How does Microsoft Copilot enable this transformation?
Sunil Kumar:
Microsoft Copilot provides a unified, enterprise-grade framework where agentic AI can be built, deployed, and scaled securely. My work integrates Microsoft 365 toolkits, Copilot Studio, Visual Studio libraries, and Azure AI Foundry to create domain-specific AI agents tailored for Healthcare and Life Sciences.
Copilot also acts as a cross-model AI platform, combining Azure OpenAI models, healthcare-optimized LLMs, and enterprise connectors into one cohesive agentic environment. This allows intelligent agents to select the best model for each task while maintaining strong security, governance, and compliance controls.
Q3: Can you walk us through the agentic workflows you build within Microsoft’s ecosystem?
Sunil Kumar:
A typical agentic workflow includes:
Designing agents in Copilot Studio or Azure AI Foundry
Connecting enterprise healthcare data using Microsoft Graph, FHIR APIs, custom connectors, and secure data gateways
Extending agent logic through Visual Studio toolkits, including function calling, reasoning chains, action groups, and orchestration layers
Deploying agents directly inside Microsoft 365 so clinicians and researchers can access them through Teams, Outlook, SharePoint, Power Apps, and other tools
The most advanced capability is multi-agent orchestration, where one agent retrieves clinical data, another analyzes it, and a third formats insights for end users. These agents work autonomously to deliver efficient and accurate outcomes.
Q4: Many organizations rely on third-party or non-Microsoft systems. How do you integrate those into the Copilot ecosystem?
Sunil Kumar:
Interoperability is a critical focus area. Many healthcare organizations already have agents built in external systems such as population health platforms, research automation tools, or specialized healthcare AI solutions.
Using REST APIs, Azure connectors, and Copilot Studio extensibility, these non-Microsoft agents are embedded into the Copilot ecosystem. This enables cross-platform orchestration, cross-model decision-making, and agent-to-agent communication-resulting in a unified, secure, enterprise-grade agentic environment rather than fragmented AI silos.
Q5: What impact have you observed across Healthcare and Life Sciences?
Sunil Kumar:
The impact has been transformative:
Clinicians significantly reduce time spent on documentation, triage, and chart reviews
Life sciences teams accelerate literature analysis, regulatory intelligence, and pharmacovigilance
Healthcare operations automate scheduling, billing, compliance checks, and workforce management
By leveraging multi-agent and cross-model AI orchestration, organizations achieve faster decision-making, higher accuracy, and reduced operational pressure-directly influencing productivity, patient outcomes, and research timelines.
Q6: Where is this technology heading next?
Sunil Kumar:
The future points toward fully agentic healthcare ecosystems. Key advancements will include agent-to-agent orchestration across clinical, operational, and research domains; cross-model AI platforms where agents dynamically select the most appropriate LLM; autonomous multi-agent systems capable of handling end-to-end workflows; and interoperable agent networks spanning EHRs, payer systems, laboratories, and research platforms.
Microsoft Copilot is rapidly becoming the central engine enabling this future, and my work is focused on building these next-generation agentic capabilities for Healthcare and Life Sciences.
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