One of the many challenges, he says, is to implement robust memory management in an agentic AI system. The process goes beyond storing and retrieving information and includes intelligently managing context, understanding the relevance of past interactions, and dynamically adapting the AI’s responses based on an evolving knowledge base.
In addition, building an agentic AI from the ground up would involve designing complex data structures, implementing efficient search algorithms, and fine-tuning the AI’s ability to interpret and prioritize information, he adds. This would require organizations to have specialized expertise in machine learning, natural language processing, and data engineering.
“By turning to specialists or adopting pre-built solutions, or tapping into the open-source ecosystem, they can leverage the expertise and experience of those who have already navigated these challenges, ultimately increasing their chances of success,” Masood says.
