A future where artificial intelligence agents talk to each other is right around the corner. According to Anshuman Chhabra, an assistant professor in USF’s Bellini College of Artificial Intelligence, Cybersecurity and Computing, agentic AI is the next frontier for machine learning models. These agents will be able to autonomously act on a user’s behalf, from writing emails and making dinner reservations to coding alongside a software developer. With agents interacting with other agents, there is a need for a better and more robust framework of engagement. Chhabra’s recent paper, “Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges,” published in IEEE Access, outlines a taxonomy of threats from agentic AI as well as opportunities to improve trust and safety. Chhabra wants to find ways to orchestrate better cooperation between models while ensuring that confidential data remains private and secure. As the leader of the Pioneering Advancements in Learning Methods (PALM) Lab in the Bellini College, he focuses on enhancing trust and safety of machine learning models and using these models in interdisciplinary applications. AI trust and safety involve a goal of interpretability, meaning that a human can understand how and why a model made a specific decision or output. “My work seeks to characterize the information flow in these AI models to understand what is driving them internally,” Chhabra said. “We want to see what the problematic behaviors are and how we can improve them by doing 'surgery' on the models. Building trust in autonomous systems The risk of agentic AI is higher than that of traditional chatbots without autonomy, especially in areas like medicine or cybersecurity. Agents, performing tasks on behalf of a user, can make mistakes such as accidentally misleading a doctor with regards to a diagnosis or deleting files in restricted codebases, leading to system failures. “At