Deontic Policies for Runtime Governance of Agentic AI Systems

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "Deontic Policies for Runtime Governance of Agentic AI Systems".

Jane: The paper was written by Anupam Joshi, Tim Finin, Karuna Joshi and Lalana Kagal from University of Maryland, Baltimore County and MIT.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Title: Tom: We're looking at a fascinating new paper today called "Deontic Policies for Runtime Governance of Agentic AI Systems." It sounds like a mouthful, but it's hitting on something we've all been worried about lately.

Jane: It really is, Tom, because as these AI agents start doing more things on their own, we need a way to keep them in check. The title mentions "deontic," which is just a fancy way of talking about the logic of duties and permissions.

Tom: So instead of just saying "yes" or "no" to an action, we're talking about what an agent is actually required to do?

Jane: Exactly, and the authors—Anupam Joshi, Tim Finin, Karuna Joshi, and Lalana Kagal from UMBC and MIT—are proposing a much deeper way to handle that.

Lu: I love how they're framing this because it moves us away from simple guardrails toward actual governance. If an agent has the power to move money or access medical records, we can't just rely on a prompt telling it to be good.

Meng: That sounds great in theory, Lu, but I wonder how these researchers from UMBC and MIT actually plan to implement that level of control without slowing everything down.

Jane: That's the big question, Meng, and it leads us directly into the core of their research.

Lalam: We should consider how this changes our relationship with technology. If we can build systems that understand their own obligations, we might finally move toward a culture of true digital responsibility.

Tom: That's a heavy thought to start with, Lalam, so let's look at what the paper actually says they've built to solve this.

Summary: Tom: The paper explains that current tools like Rego or Cedar are too limited because they only handle permissions and prohibitions. They can tell an agent "you can't do that," but they struggle with "you did that, so now you must do this."

Jane: Right, and the authors argue that if an agent installs software, it might have a duty to notify a security officer immediately after. Current systems don't have a native way to manage those kinds of follow-up tasks.

Meng: I noticed they've developed something called AgenticRei to fix this. How does it actually sit between the AI and the real world?

Jane: It uses a three-step process where it extracts the action into a simple triple, evaluates it against a logic engine, and then applies the decision.

Tom: And they're doing all of this using an engine called RDFox that lives entirely outside of the Large Language Model.

Lu: That's the most brilliant part to me! By keeping the decision-making process separate from the LLM, they avoid that "reasoning drift" where a model might misinterpret its own security rules.

Meng: I was looking at their performance numbers, and they claim these decisions happen in under ten milliseconds. If that's true, it could actually work in real-time production environments.

Lalam: It provides a deterministic safety net that doesn't care how much the AI's reasoning fluctuates. This creates a world where we can trust the outcome even if the process is unpredictable.

Jane: That reliability is exactly what's missing, so let's talk about the specific improvements they suggest to make this better than anything we have now.

Improvements: Tom: The paper really pushes for more than just simple rules; they want "meta-policies" that can resolve conflicts when two different rules clash.

Jane: Imagine a situation where a company policy says "don't export data," but a legal subpoena says "you must export this specific data." AgenticRei uses these meta-policies to decide which rule wins based on logic rather than just which one was written first.

Meng: They also talk about something called semantic grounding, which sounds like it could save developers a lot of headache.

Jane: It does, because instead of listing every single type of sensitive file, you can just tell the system that anything belonging to a certain category is off-limits.

Tom: Like if they define "Protected Health Information" as a broad class, the system automatically knows that a "pediatric oncology record" is also protected because it's a subclass.

Lu: That's so much more elegant than the old way of manually updating every single rule whenever a new data type appears! It allows the governance to evolve naturally alongside the data itself.

Meng: I was reading their financial services example where an agent is allowed to make a high-value transaction only if it presents a specific credential and then immediately fulfills an obligation to file a report.

Jane: That's a perfect example of how they combine permissions, credentials, and duties all in one go.

Lalam: This level of detail turns accountability from an afterthought into a built-in feature of the system. It allows us to build complex societies of agents that actually follow the spirit of our laws.

Tom: It's a massive leap forward in how we think about controlling these autonomous systems.

Conclusion: Jane: We've covered a lot today, from the basic idea of deontic logic to the way AgenticRei uses ontologies to handle complex data hierarchies.

Tom: It really seems like the researchers are providing a blueprint for how we can finally move agentic AI from experimental toys to reliable enterprise tools.

Lu: I'm thinking about how this could enable entire ecosystems of agents that can cooperate across different organizations while still respecting everyone's private rules!

Meng: From my side, seeing that sub-ten-millisecond latency makes me think this is actually something we could start implementing in our stacks very soon.

Lalam: If we embrace this, we aren't just building smarter tools; we are building a foundation for a more stable and predictable digital culture.

Jane: Well, thank you all for joining us to discuss "Deontic Policies for Runtime Governance of Agentic AI Systems."

Tom: We'll be back with another paper soon, so stay tuned!