From the course: Building AI That Remembers: Architecting Reliable, Context-Aware Enterprise Agents
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Architecting agents with built-in memory
From the course: Building AI That Remembers: Architecting Reliable, Context-Aware Enterprise Agents
Architecting agents with built-in memory
Let's put all of this together. Architecting an agent extends way beyond what LLM model to choose, to how that model actually connects to your enterprise infrastructure. Memory-enabled agents require four distinct layers. Now, if you miss any one of the four, you'll either create forgetful agents without memory, or dangerous agents without governance. Let's look at those four cores. The LLM core functions as the planner. This layer receives a goal from a customer and uses agentic reasoning to decompose it. It processes facts rather than storing them. The memory core provides the knowledge layer where state persists. So an integrated AI database that houses episodic memory, things like container histories, and semantic memory, and this is what grounds your enterprise data through RAG. Remember, we talked about that retrieval, augmented generation, and procedural memory. This stores the how-to library for your typical business tasks. Another core, the toolset, forms the execution layer,…
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