Systemic approach to memory, knowledge, and context in agentic AI architectures
InfoQ published an article arguing that as AI moves from chatbots to autonomous agents the key engineering challenge is managing what an agent knows, remembers, and sees in real time. It recommends separating context, memory, and knowledge, building a 'context engine' or control plane to select and deliver minimal high-value context to LLMs, and optimizing context to reduce hallucinations and operational costs.