AI agents are shifting from hype to real, immediate impact. But with that power comes risk. As healthcare organizations experiment with autonomous systems that analyze, decide, and act across clinical and administrative workflows, the need for real-time governance is no longer optional. It’s foundational.
In our recent episode of The Health/Tech Edge, a podcast dedicated to the latest trends and innovations transforming healthcare, Raheel Retiwalla, Chief Strategy Officer at Productive Edge, joined host, Mike Moore, to unpack the white paper AI Governance, Compliance, and Risk Management: A Responsible AI Framework for Healthcare AI Agents. Together, they explore why AI agents represent a paradigm shift in care delivery and operational execution and how a governance-first approach enables organizations to innovate with confidence. From observability and real-time oversight to risk classification and pilot strategy, this episode offers a practical blueprint for deploying AI responsibly at scale.
Listen to the full discussion or keep on reading the summarized transcription of the episode to learn more about the importance of deploying AI responsibly, with embedded oversight, compliance, and control from the start.
Mike Moore: Hello, and welcome to The Health/Tech Edge. My name's Mike Moore. I'll be your host. Today's guest is Raheel Retiwalla, Chief Strategy Officer of Productive Edge.
Raheel Retiwalla: Hi, Mike. How are you?
Mike Moore: I'm doing great, thanks. I know you've just recently completed a white paper talking about governance specifically around AI agents in healthcare.
It's a hot topic, everything AI agent related. But specifically for healthcare organizations, I know you feel that there's a sense of urgency to establish a governance framework, and that's what this paper you've written is about. So I'd like to get into it and really just start looking at this kind of need.
You've said that the paper really talks about rethinking operations and rewiring care delivery. Let me take that again. So the paper talks about AI agents and rethinking operations and rewiring care delivery. What makes AI agents different from previous AI applications, and why are they such a powerful new paradigm for healthcare right now?
Raheel Retiwalla: It's a really opportune time, Mike. Traditional AI has been employed at healthcare organizations for a long time. We've done a lot of work in predictive models. We've done a lot of rules-based automation in healthcare across a wide variety of different operational functions and use cases.
And it served us well. Traditional AI has given us insights like forecasting, risk scores, and recommendations, but a lot of the follow-on work to execute and close the loop was always up to staff, up to the people working to get care done. What AI agents do is that they take it further; they can analyze, they can decide, they can act, and they can even collaborate with other agents.
You can think of them as autonomous units, not just a model, a predictive model. And that's what makes them really powerful. So in healthcare, for example, where we have thousands of manual, rules-driven, error-prone processes, agents can help us rewire those. Instead of humans navigating broken systems, agents can help navigate the system on their behalf.
And that's where the power of AI agents comes in. And when I said earlier it is the exciting part, it is that the agents are finally making it possible for us to rethink how. Care gets delivered and how operations run. So it's not about optimizing tasks but actually redesigning the workflow.
And that opens the door to real transformation, not just incremental gains.
Mike Moore: That's great. I know the paper also highlights the risks, like unintended biases, data privacy violations, non-compliance, and ultimately a loss of transparency. There are going to be questions about how the AI arrived at a decision or a recommendation or even just summarizing information.
So why are the risks particularly pronounced with AI agents in healthcare compared to some simpler AI tools that the teams might have worked with?
Raheel Retiwalla: Largely because you can have agents that don't just make a recommendation; they can actually take action. They can send messages; they can do nudges, summarize clinical data, or even route things.
So it depends, of course, on the complexity of the use case that you're deploying and the capability of the agent. But they can do more than give recommendations. So if there's a bias, a data error, or a logic flaw, it doesn't stay hidden in a report where you, in the past, would've received a segmentation report.
This is beyond that. This becomes a decision, a communication, or even a denial of service. And the dynamic nature of agents means. That their behavior can evolve over time. As situations change, context evolves, and more information is processed. So we need real-time oversight, not just a periodic review of these agents.
Mike Moore: That's great. So let's dig into the framework now because you're advocating in the paper for a governance-first architecture that's built on three layers. Can you briefly explain the layers, the AI agents, the AI governance framework, and the AI platform infrastructure and how those are designed to interoperate and create what you call this trustworthy autonomous AI ecosystem?
Raheel Retiwalla: Sure. So the main thing to recognize is that we designed this framework with trust, control, and scale in mind. So at the bottom, you have the AI platform. We call it the AI platform infrastructure. This is the secure, scalable foundation. It includes the logging systems, the observability tools, the model gateways, and the compliance checks.
That keeps the ecosystem stable and compliant. On top of that, you have the AI governance framework. This is, see this and view this as the control plane, right? It enforces policies. It manages risk levels for what the agent is doing or expected to do, ensures auditability, and helps track the agent's behavior over time.
This governance isn't optional. It's essentially baked into the architecture. And the top layer are the agents themselves. So these are the autonomous decision-making units. They analyze data, they decide on next steps, and they can even interact with humans or other agents. And we also focus on what we call the agent experience.
They are making them explainable, responsive, and easy to collaborate with. So we can instrument them. We are able to log everything that is happening, and we're able to have them explain themselves and be able to react to that in real time.
Mike Moore: So I know governance; obviously, it's central to the framework, not an afterthought here. So maybe you can talk more about the core capabilities of this AI governance framework and why these capabilities are really essential. Teams obviously want to be innovative. They want to really find opportunities, especially to accelerate the pace of change when it comes to saving costs and improving productivity—some of those things—but having to do that in a balanced way, especially with safety and regulatory requirements. So tell us more about the core capabilities in this framework.
Raheel Retiwalla: The framework of the white paper goes into a lot of detail. But if you sum it up at a very high level, these agents are not static, right? They evolve. They of course, can be retrained, and they respond to real world inputs.
So the three specific abilities and capabilities that we look for when we are implementing governance are, first, observability. So we, this ensures that we can see what an agent is doing in real-time compliance. So we ensure that agents are following regulatory and enterprise rules and testability so we can ensure that we can validate their behavior before and after deployment.
So these are the guardrails that let us innovate safely. And the white paper goes into a lot more detail as to the modular capabilities of each of these three areas. And how do you essentially implement them and enable them within your overall framework.
Mike Moore: Now I have to imagine too, that as you talk with healthcare leaders and consider the different use cases that they're exploring for the use of AI agents, all things are not equal, right?
There are some differences, and some of them are probably easier, some of them much more complex. And I think you've got a risk classification model that is helpful in assessing how hard some of these will hit and where, from a risk point of view, they line up.
So do you want to talk about your risk classification model?
Raheel Retiwalla: At the heart of our approach, and I think most organizations need to consider something like this, has to be an enablement for teams that are looking to either deploy agents or build agents and deploy them.
So as an organization, we look at the opportunity, especially to allow governance to be in the forefront and not an afterthought. And the way you do that is essentially allow people to assess the risk of their use case of what the agent is going to be deployed for. So what is the operational compliance and regulatory risk of that agent?
And you can help them go through some guidelines as to, hey, here are some examples of low risk, medium risk, high risk, or more complex risk, and based on that risk profile, then you are essentially enabling the teams with the right level of governance controls that should be enabled within that agent or agent solution to ensure that it meets that particular risk profile. Then you can go beyond that.
In addition to governance controls, you can help teams understand what solution capabilities they will need to deliver on those governance controls and then help them understand what they need to do to go from where they might be today. To be able to actually implement those capabilities within your environment.
So this enablement is really critical, and it starts with and begins with the risk classification. Once you have that, and we've given some examples in the white paper, of course, not just of the risk, but of examples of some types of agents that could be classified as lower risk or medium risk, then helps you see how to connect the dots to some governance controls that you can put in.
Mike Moore: That's great. And I think that teams will find that helpful because everyone has some goals this year around doing something with AI agents and being able to move at a good pace, but doing so in a safe and managed-risk fashion, I'm sure, will be beneficial to all. In fact, thinking about kind of the way things run over time, I know you're talking in the paper about real-time governance and policy intervention.
How does this differ from the traditional retrospective kind of audits that organizations would execute normally? And especially as we think about AI agents and their behavior, how that might vary over time in kind of a learning model. How do these audits play a role in that?
Raheel Retiwalla: As you said, traditional AI audits have happened after the fact. It could be weeks or even months later, right? But agents are actually acting in real time across workflows, perhaps not just within the organization but connecting dots across organizations as well. So real time governance essentially means that the system is watching as the agent operates.
It's logging, it's validating, and even intervening if needed. And that's where the three-layer architecture that we've proposed in the white paper is built to support the bidirectional information that is required to allow the governance framework to be an active participant in the observability and be able to have the right level of intervention and be able to deploy that on the agent side as needed.
So think of it as life compliance, not a retrospective review. And that's the only way to safely scale autonomous systems in healthcare or, honestly, in any other industry. How to get there? That's something we explore in the white paper. And we presented a reference architecture that can assist in your thinking as to how to lay the groundwork for that.
But it's not an overnight thing you can just turn on. It's something that you build as part of your overall roadmap as well.
Mike Moore: Which is helpful. I think especially in technology rollouts, most organizations have a process by which they'll run a pilot or proof of concept before things go broader.
You don't just release it to the full organization and let it go, and it sounds like this process that you're advocating gives a way to keep that monitoring going through all those different phases, especially the widespread adoption for the organization so that we're driving the right kind of behavior.
As AI gets involved in different workflows and processes. So pivoting with that and thinking about implementing this governance architecture might seem a little daunting, right? AI can be daunting. Especially a lot of organizations are still trying to manage their digital transformation initiatives, and now AI is coming into the mix, and boards or stakeholders are asking for more rapid innovation.
What's the approach that you recommend for healthcare organizations as they're just starting out? What's maybe a first step or a low-risk kind of place that they can go to as they start looking at this opportunity?
Raheel Retiwalla: The fundamental thing is to not start with, of course, the highest risk and the most complex workflow. Start where the stakes are low, but the inefficiencies are real. We've been working a lot with clients in helping identify where that, where the quadrant is, where the impact is high from an inefficiency perspective.
But the fee and the feasibility are high as well. A lot of summarization capabilities are easy wins because processes are pretty complex to follow for staff, as an example, and you can certainly assist them in helping them get better and more accurate information as to what steps they need to go through based on certain regulatory compliance processes. So those are easy benefit inquiries, agent assist, and contact centers; these are highly efficient but low in terms of risk. So these are ideal pilots not just because they're measurable and governable, but also because they're easy to explain and give you the ability to get your feet wet with agentic architecture.
But also, start to put those governance guardrails in place and start testing and validating. Your ability to govern them. In our white paper, we've documented a section called Start Fast, Govern Smart, and Scale, right? It's an approach. That allows you to validate the value rapidly in governance and expand from there.
Mike Moore: Yeah, that's great. And I think that for a lot of organizations, whether they're a healthcare provider or a payer, interacting with patients and members, getting that summarization to I think about my doctor and my annual physical, right?
We haven't seen each other much in the last year. He's going in and trying to figure out what's going on with me. That kind of summarization of information and making it at the fingertips of a clinician, an administrator, or a customer service agent is just helpful for everyone to pull that information together.
So as we wrap up here, what's the single most important takeaway that you want healthcare, technology, and operations leaders to take away from this white paper?
Raheel Retiwalla: I would say that AI agents are not just a new feature, right? They're thinking of them as a new paradigm, but with that power comes responsibility.
And the only way to scale safely is with governance at the core, not bolted on later. So if you start with the right foundation—observability, risk classification, platform standards, governance controls, all the things that we've talked about in our white paper, and give you the reference architecture to start, you can start putting these things in place.
Then you can unlock massive transformation without compromising safety and trust. And that would be my recommendation, Mike.
Mike Moore: That's excellent. And I think it's an opportunity if you look at it in a positive way. The rewiring of workflows and processes, putting AI really at the core of the innovation and leveraging its awesome power, and doing it in a responsible way that helps your organization go farther, reducing costs, improving care, improving productivity, whatever your goals are for the organization, AI can be a great and powerful tool in doing so.
Raheel Retiwalla: The one thing I'll mention, just to add onto that, is that all of this is, of course, change, right?
And workflows in healthcare haven't changed in a long time, and you're talking about rewiring, and those are not light words to take in. This particular conversation is about governance, but change management and how we think about those use cases and how they affect longstanding workflows and the impact on people that has to also be considered in part, right? So we don't want to make it just sound like, just plug them in. There is a lot of thinking that needs to go through from that perspective as well.
And how do we measure their performance? How we think about impact are also elements of the overall strategy. Governance is, of course, a core component of that, which was this conversation.
Mike Moore: That’s a great way to end it. Appreciate the conversation today around governance for AI agents and healthcare.
Designing AI For Impact and Integrity
AI agents are not just another technology layer but are rapidly growing into a new operating model. However, safe, scalable innovation in healthcare requires more than technical capability. It demands a foundation of embedded governance that accounts for evolving agent behavior, risk exposure, and organizational readiness.
From piloting low-risk use cases to implementing layered architecture with observability, testability, and compliance, the message is clear: real transformation is possible when AI is governed by design, not by correction.
Download the full white paper to dive deeper into Productive Edge's responsible AI framework for healthcare AI agents and start building the operational and ethical scaffolding to bring your AI strategy to life.
whitepaper
AI Governance, Compliance, and Risk Management
Unlock a responsible framework for AI agents in healthcare.
