10 hours ago
To Enter The Agentic AI Era, You Need To Know About BDI
As businesses delve into agentic AI, they need a team that can explain its choices, models, and ... More functionality.
When businesses look to tap into the power of a new technological trend, they can be tempted to put their faith in third-party vendors without developing an understanding of how the vendors' tools work. But as the global best practices organization Shared Assessments explains, 'Properly understanding and managing third-party vendors is key to ensuring your organization remains secure, compliant, and resilient.'
As Nextiva's CEO, I work to equip organizations of all kinds with AI-driven platforms to deliver the best customer experiences. I'm a big believer in the importance of educating businesses about what they're getting and how it works. That's why my team is active about providing explanations of all sorts of emerging technologies, helping everyone make sense of them.
Case in point is agentic AI. As organizations invest in this advanced level of automation, they should look into the kinds of systems any tool uses. One of those is known as BDI, which stands for Belief-Desire-Intention models.
These models can go a long way in making or breaking an agentic AI system. A study published this year in the International Journal of Scientific Research in Computer Science, Engineering and Information Technology explains, 'The Belief-Desire-Intention model has established itself as a pivotal methodology in agentic framework implementation, particularly in hybrid agent architectures. Empirical studies demonstrate that concurrent BDI-based systems achieve a 68.4% improvement in decision-making accuracy.'
Focusing on financial trading environments, researcher Sreeram Reddy Thoom of India's JNTU (Jawaharlal Nehru Technological University) found that in high-frequency trading scenarios, this model reduces latency 'by an average of 38.7% while maintaining execution accuracy above 99.1%.'
Simulating how people think
BDI models are designed to follow 'human-like reasoning and decision-making processes with concepts derived from folk psychology,' according to a recent study in Engineering Applications of Artificial Intelligence. Austrian researchers Laurent Frering and Gerald Steinbauer-Wagner of Graz University and Andreas Holzinger BOKU University note that these model has a proven ability 'to perform verifiable reasoning and goal management,' and to 'store its mind state, including its belief base' and more.
As agentic AI takes off, we can expect more technologists and other scientists to keep building on these models, developing tweaks and changes to advance them further. It's one of the many reasons that agentic AI is so promising. The more it can handle certain tasks, the more humans are freed up to focus on more complex responsibilities that require their direct involvement.
As I recently told the Business Standard, AI elevates human intelligence. It remembers, reasons, and helps agents be more effective. Ultimately, it helps them deliver the most important thing they can: excellent CX (customer experiences).
Beware the pitfalls
As exciting as agentic AI with BDI models can be, there are also potential downsides. For example, if a tool is hard coded and not built to keep learning and changing, it may fail to adapt, and instead repeat processes that fail to deliver results (an idea Omer Ibrahim Erduran of Goethe University warned about in a study). Any agentic AI tool needs to be built to evolve over time, responding to all kinds of changes -- especially in customer behaviors, which are shifting at a faster pace than ever before.
And, perhaps ironically, BDI models can lead to a problem of overthinking. Researchers warn of computational overhead that can come along with these systems. So in addition to trying to think the way people do (only faster), these models also need to be taught when to stop thinking. As researchers Ramira van der Meulen, Rineke Verbrugge, and Max van Duijn recently put it in their own recent study, 'In everyday interaction, humans are more likely to use reasoning shortcuts than to overanalyse the situation. A system designed to understand human perspectives should take this into account.'
These complexities all lead to the same conclusion: As you delve into agentic AI, don't do so blindly. Work with a team that can explain its choices, models, and functionality. Familiarize yourself enough with the elements that drive agentic AI that you know what to look for, and what to ask.
A vendor worth your investment will provide all that, equipping you with not just the tools but the knowledge you need to help guide your organization into the agentic AI era.
