Context and Memory Engineering in Agentic AI Systems Explained

An article discusses the distinctions between context engineering and memory engineering in agentic AI systems. It highlights how both disciplines address different challenges and their interaction when retrieved memory enters the context window.

As AI agents engage in lengthy workflows and multi-session use cases, issues arise such as mid-task constraint drops, inappropriate information retrieval, and context bleeding from prior steps. These failures often stem from the intertwined nature of context and memory engineering, which, while related, function differently and require distinct systems.

Context engineering pertains to designing a single inference call, determining what information to include, compress, or discard. This process is temporary, as the context window resets when the call concludes. Conversely, memory engineering focuses on what information remains beyond a single interaction, managing the systems and policies for writing, storing, retrieving, updating, and governing data for future interactions.

The article elaborates on context engineering, explaining it involves assembling an optimal context window from various sources like system prompts, task descriptions, and conversation history. Not all available information should be included; decisions on inclusion, compression, and omission are crucial design choices. The arrangement of information within the context window significantly affects a model's ability to utilize it effectively, with the “lost in the middle” effect suggesting that content at the beginning and end of long contexts is weighted more heavily.

Memory engineering, on the other hand, determines what persists after an inference call, focusing on write policies, storage, retrieval, and maintaining accuracy over time. A well-defined write policy is essential for ensuring memory quality, as poorly defined policies can lead to information overload and degraded retrieval quality. Different memory types and storage backends serve varied purposes, influencing available retrieval strategies.