Understanding Agentic AI & Protocols: Use Cases, Variants, and Real-World Fit
The future of TPRM is evolving. Are you prepared to navigate the rise of Agentic AI?
Written by: The Black Kite Research Group led by Müzeyyen Gökçen Tapkan, Director of Data Research
Agentic AI systems are designed to take the initiative, make decisions, and pursue goals autonomously. But before you jump in, you need to understand not only what agentic AI is, but how it’s designed, how it works, and the challenges and opportunities it presents.
In this research paper, we explore the foundational concepts and protocols you need to know to future-proof your TPRM operations.
You'll learn:
- The Key Concepts: How AI agents differ from traditional models and what capabilities they bring to the table.
- The Foundational Protocols: A deep dive into the protocols that will shape the future of AI collaboration, including Model Context Protocol (MCP), Agent-to-Agent (A2A), and LangChain Expression Language (LCEL).
- The Remaining Challenges: We highlight the critical issues that still need to be addressed, from ensuring long-term memory to mitigating security blind spots.
This report is your guide to preparing for the next evolution of intelligent automation. Read it now to get a head start on preparing for the future of third-party risk management and to see how Black Kite is leveraging these advancements today.