Externalized Graph Memory (DAG-Based State Infrastructure)
Date of Addition
7 Sept 2026
Basically, it’s the idea of separating an AI agent’s “working memory” from the short-lived, fuzzy prompt window it’s stuck with during a chat.
Instead of cramming everything into the context, the system writes intermediate reasoning steps, tool results, and shared project state into an external, deterministic store, often something like a Directed Acyclic Graph (DAG).
That way you don’t get runaway context bloat, made-up dependencies, or the classic “wait, where did that state go?” problem when multiple agents hand work off to each other.
The end result is a shift from fragile, prompt-bound chat loops to something you can actually audit and run like real enterprise infrastructure.
This definition is also a part of the upcoming AI Diffusion Risk Guide by Indic Pacific Legal Research and indiaml.org.
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