RESEARCH · RESEARCH · #2920
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.
KEY POINTS
- 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.
- Clarifies an architectural shift from prompt engineering to systems-level 'context engineering,' outlining concrete layers (context, memory, knowledge) and a context engine that can improve reliability and reduce hallucinations in production agents.
WHY IT MATTERS
Clarifies an architectural shift from prompt engineering to systems-level 'context engineering,' outlining concrete layers (context, memory, knowledge) and a context engine that can improve reliability and reduce hallucinations in production agents.