· ai-devtools
Article: Systemic Approach to Memory, Knowledge, and Context in Agentic AI Architectures
This article is part of the "Agentic AI Architecture" article series.
In this series, we try to establish agentic AI architecture as a new type of software architecture that will likely dominate the industry for years to come. The articles, written by industry experts, cover various elements and aspects of agentic AI architecture.
We aim to present the latest trends and developments shaping the new type of architecture as it enters the mainstream. As the industry moves from chatbots to autonomous agents, the architectural bottleneck is shifting.
The hard problem is no longer simply retrieving data. It is deciding what an agent should know, when it should know it, how long it should remember it, and what should be forgotten.
As with humans, it is one thing to know and another to remember. Knowing when to use any of the existing "knows" by bringing it to memory depends on the task context.
For example, if I want to cook an omelet, I don't necessarily need to remember how to knead dough right now. Agents must reason across tools, sessions, users, and live systems, given that in an engineered system we can describe the context as a control plane.
Earlier coverage
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- Microsoft's moolah mountain is more massive than ever. It just doesn't want the people who built it Nov 2
- From Expertise to Intelligence: NISCO Deploys World's First Swarm-Intelligence Steel Model at Scale Nov 2
