Agents can generate code, but ensuring correctness for systems, team conventions, and past decisions creates correction loops, wasting time and tokens. More MCPs, rules, and larger context windows provide agents access to information, yet not true understanding. Teams leading the AI maturity curve utilize a context layer to give agents exactly what they need for the task. A free webinar on Sep 23 explores where teams get stuck and why common fixes fall short, demonstrating how a context layer solves for quality, efficiency, and cost.

LLMs can analyze a 100-page document and refer back to previous messages, but open a new chat, and the earlier conversation vanishes. It appears LLMs have the memory of a goldfish. This is true; LLMs usually have no personal or persistent memory of previous interactions. However, they refer to past discussion because the application built around the model supplies information with each new message. The model itself does not remember; the surrounding application handles the recall by storing messages, maintaining summaries, retrieving relevant memories, and maintaining user profiles. From the user's perspective, the model appears to remember, but technically, the application is doing most of the work.

As a conversation grows, the application must process more text, increasing cost and latency. Eventually, conversation size exceeds the model's context window, requiring older information to be removed, summarized, or stored elsewhere. In LLM contexts, "memory" describes several distinct concepts that should not be confused. During training, an LLM learns patterns from enormous data, encoded in billions of parameters or weights. This enables the model to explain Java Script or write an email without receiving that knowledge in the current prompt. However, this is not personal memory. If a user states, "My preferred programming language is Type Script," a normal API response does not rewrite the model's weights. The base model does not permanently learn that fact. The model's temporary working memory is its context window, containing system instructions, the current user message, previous messages, retrieved documents, tool descriptions, saved preferences, and conversation summaries. The context window is closer to a desk than human memory. Once the desk is cleared, the model cannot recover documents unless the application places them there again. Persistent memory normally lives outside the model in a database, file, vector store, or profile service. When the model needs that information, the application retrieves and places it in the context window.