Article: Systemic Approach to Memory, Knowledge, and Context in Agentic AI Architectures

The article argues that reliable agentic AI architecture depends on a context engine that selects, injects, compresses, and expires information before it reaches the model. It separates context, memory, and knowledge, and recommends tiered memory, curated knowledge, and token budgeting. It also covers common anti-patterns, such as context stuffing and stale data loops, with mitigations. By Adi Polak

• #memory
• #large language models