The memory layer, and the format it's written in

Every file in this bundle — this one included — is plain markdown with a few lines of YAML at the top. The format is OKF, the Open Knowledge Format (plain names: an LLM wiki, a knowledge base as files). It's the memory-layer half of how a glass agent's context is assembled.

What OKF is

OKF is an open standard for the curated knowledge AI systems need, published by Google Cloud's Data Cloud team (v0.1, June 2026). Google Cloud describes it as "a vendor-neutral, agent- and human-friendly standard for representing the metadata, context, and curated knowledge that modern AI systems need" — their definition, attributed. It formalizes a pattern Andrej Karpathy called the "LLM wiki": knowledge written once, in files, that both people and models can read.

The whole idea is deliberately small:

Just markdown. Readable in any editor, renders on GitHub, needs no special tool.
Just files. A directory you can ship as a folder, host in a git repo, or mount on a filesystem — no database, no runtime, no SDK, no account.
Just YAML frontmatter. A few structured fields at the top of each file make it self-describing and machine-navigable; the prose below is for humans.
A knowledge graph from plain links. Files reference each other with ordinary markdown links, so a bundle is a small connected wiki, not a pile of documents.

Why Thalatta uses it

OKF is the format version of the thing Thalatta sells: appropriate, legible context — in the open.

One file, two readers. The exact text the agent works from is the exact text a person can read. No "what did the model actually see" gap.
Governed like code. It lives in a git repo, so every change to what an agent says about Thalatta is reviewed, dated, and reversible.
No platform to buy. Because it's files, not a product, the knowledge is portable and outlives any one vendor's tool — the same posture Thalatta takes with every system it builds.

Given, not searched

A glass agent never starts from zero. Before it answers, a broker assembles its context — the caller's own record plus the right slice of the memory layer — so it begins already knowing what the company knows. The usual alternative, handing the agent a search tool and letting it retrieve chunks reactively mid-answer (traditional RAG), makes the agent fish: slow, costly, and repeated on every run. Assembled context flips it: gather what the job needs and hand it to the agent before it answers. Deterministic, auditable, and cheap, because the agent spends its budget on the work instead of the search. (This isn't "never retrieve" — when the library is large you still select a subset, but up front, from a governed index you can read.)

A curated subset, on purpose — and tiers of depth

No bundle should hold everything a company knows. This public one deliberately doesn't: it's the Surface — curated for anyone, human or AI, who sails past. A fuller briefing (the Harbor) is what the live glass agent actually reads, open to signed-in visitors at thalatta.ai/okf — the same files the assistant reads, which is the point of glass. And some slices are gated deeper still, injected server-side only for people Jeff has personally flagged — no amount of asking can summon them into a session that isn't entitled to them. Same memory layer, tiered boundaries: one governed by curation, one enforced in the data, outside the agent's reach.

"Appropriate context, not maximum context" isn't a slogan here; it's literally how the layers are built.