AI agents are systems that utilize large language models to plan and execute actions. They will significantly impact the workforce in 2025, affecting productivity and efficiency. Building agentic systems is a complex field with challenges such as developing reasoning tasks-specialized models and managing memory effectively. Memory is crucial for AI agents to learn from past interactions, retain information, and maintain context. AI agents rely on both short-term and long-term memory, with short-term memory working like computer RAM holding onto relevant details for an ongoing task or conversation. Long-term memory works more like a hard drive, storing vast amounts of information to be accessed later. Managing long-term memory is complex due to challenges such as deciding which type of memories to store and retrieve effectively into working memory. Redis stands out as the ideal data platform for agentic memory due to its ability to efficiently store and retrieve memories quickly.