A Field Guide to the Distributed AI Economy

Glossary

37 terms, each tagged by provenance — the guide's convention, kept intact.

W: Defined by the whitepaper
I: Standard industry or regulatory usage
G: Framing introduced by this guide

Accountability loopW — Defined by the whitepaper: The cycle by which validated AI work improves the system that produced it: Frames orient, Cogs perform, Ops orchestrate, Guards validate, Gates control, Tracks preserve, Organizational Memory learns, artifacts improve.
AgentI — Standard industry or regulatory usage: Industry term for AI systems that act with some autonomy, using tools toward goals. Overloaded in practice; this architecture deliberately unbundles it into Cogs (workers) and Ops (supervised workflows). See §4.
CogW — Defined by the whitepaper: A discrete AI worker: a model plus a Frame-founded context plus tools plus governance parameters. The atomic, auditable unit of AI work. See §4.
Context managementW — Defined by the whitepaperI — Standard industry or regulatory usage: The discipline of deciding what a model sees, in what order, under what constraints. In this architecture, Frames form the governed foundation of a Cog's context, with retrieval, memory, and task instructions assembled around them at invocation.
Coordination goodG — Framing introduced by this guide: An economics term for something valuable because others adopt it too (standards, vocabularies, protocols). The guide's explanation for why most Frames circulate free. See §10.
Desktop ApplicationW — Defined by the whitepaper: The interface layer through which knowledge workers run Ops, converse with Frame-oriented Cogs, and install, compose, and share Frames; home of Local Memory. See §9.
Distributed AI economyW — Defined by the whitepaper: The paper's name for an economy of AI artifacts (Ops, Cogs, Frames, Guards) exchanged across a network of independently owned Intelligence Hubs, rather than through one central platform.
EU AI ActI — Standard industry or regulatory usage: Regulation (EU) 2024/1689. Its high-risk provisions (active 2026) require human oversight (Article 14) and automatic event recording with retention (Articles 12, 19), which map onto Gates and Tracks respectively. Noncompliance with the high-risk obligations carries fines up to €15M or 3% of global turnover; the Act's top tier (€35M or 7%) applies to prohibited practices.
FrameW — Defined by the whitepaper: A scoped, versioned, text-based artifact carrying organizational context: rules, terminology, goals, style, norms, skills, tool expectations, prompts, and process details. Scoped, inheritable, composable, shareable, discoverable. See §3.
Frame compositionW — Defined by the whitepaper: Combining multiple Frames for a working session (company + department + project + installed external Frames), with the layering managed automatically.
Frame inheritanceW — Defined by the whitepaper: A child Frame extending a parent (project inherits department inherits company), so context propagates down organizational structure with an auditable chain.
Frame protocolW — Defined by the whitepaper: The open specification for how Frames are structured, making them exchangeable across organizations and implementations.
GateW — Defined by the whitepaper: A decision point in an Op where Guard results determine what happens next: proceed, pause, escalate, retry, or stop. Guards check; Gates decide. See §6.
GuardW — Defined by the whitepaper: A reusable verification component that checks whether AI output or action is correct, safe, and policy-compliant. Seven categories in the paper: algorithmic, source-grounding, consensus, expert, policy & safety, regression & drift, outcome. See §6.
Human-in-the-loop checkpointW — Defined by the whitepaperI — Standard industry or regulatory usage: A declared point in an Op where a human approves, refines, or redirects the work before it proceeds.
Intelligence HubW — Defined by the whitepaper: A sovereign, governed AI deployment inside an organization's own infrastructure perimeter: where Frames live, Cogs run, Ops execute, Tracks accumulate, and the organization connects to the marketplace. See §7.
Intentional vs. emergent contextW — Defined by the whitepaper: Context the organization deliberately encodes (Frames) versus context generated by AI work actually happening (conversations, executions, corrections). Organizational Memory holds both.
Local MemoryW — Defined by the whitepaper: The private, user-side memory in the Desktop Application holding an individual's composed Frames and working context; promotable to shared status only by the user. See §9.
ManifestW — Defined by the whitepaperI — Standard industry or regulatory usage: The declaration packaged with an Op specifying its dependencies, environment, configuration, Frames, and Validation Strategy; what makes an Op installable and auditable.
NebariW — Defined by the whitepaper: OpenTeams' open-source, modular stack for deploying and managing AI infrastructure reproducibly; a curated, additive assembly of open-source components. See §8.
NebiW — Defined by the whitepaper: The packaging and environment-management layer: the common format in which Frames, Cogs, and Ops are specified, versioned, installed, and rolled back. The architecture's pip/npm analog. See §8.
NIST AI RMFI — Standard industry or regulatory usage: The U.S. National Institute of Standards and Technology's AI Risk Management Framework (govern, map, measure, manage), with a Generative AI Profile; a declared Validation Strategy is how an Op makes it executable.
Non-rivalryG — Framing introduced by this guide: The economic property that using something does not deplete it. Organizational context is non-rival, which is why sharing a Frame can increase rather than decrease its value to the owner. See §2, §10.
OpW — Defined by the whitepaper: An installable, versioned, supervised program of AI-driven work mapping to a whole job ("close the books"). Composes Cogs, workflow Frames, a supervising model, human checkpoints, integration logic, a declared Validation Strategy, and a manifest. See §5.
Open-weight modelI — Standard industry or regulatory usage: A model whose weights (its trained numerical parameters, the artifact that constitutes the model) are published and can be run, inspected, and hosted by anyone; a precondition for model-level control points inside a Hub.
Organizational contextW — Defined by the whitepaper: The rules, terminology, goals, style, norms, and process knowledge within which an organization's work happens; the asset whose leakage Part I diagnoses.
Organizational MemoryW — Defined by the whitepaper: The Hub's persistent context substrate, accumulating intentional and emergent context under the organization's governance, with access controls, retention policy, and the ability to forget. See §7.
Platform vs. protocol economyG — Framing introduced by this guide: Guide framing: platforms have an owner who intermediates and taxes exchange; protocols have no owner at the exchange layer and win adoption instead. This architecture is structurally a protocol play. See §11.
Prompt injectionI — Standard industry or regulatory usage: Adversarial input, planted in a prompt or in content a model reads (a document, email, or web page), that overrides the model's intended instructions so it acts on the attacker's behalf. Ranked the top risk in the OWASP Top 10 for LLM Applications (November 2024). See §6, FAQ 11.
RAG (retrieval-augmented generation)I — Standard industry or regulatory usage: Supplying a model with retrieved documents at query time. A technique this architecture uses inside Cog context assembly, not a substitute for governed context. See FAQ 3.
Rented intelligenceW — Defined by the whitepaper: The paper's term for consuming AI through vendor-controlled services that the organization cannot inspect, reproduce, audit, or own. See §1.
ReproducibilityW — Defined by the whitepaper: The guarantee that an artifact installed in one Hub is the same system (versions, dependencies, environment, configuration) as in another, "within generative AI limits": environments reproduce; stochastic outputs are verified by Guards instead. See §8, FAQ 13.
ScopeW — Defined by the whitepaper: The defined range a Frame applies to: organization, department, team, project, role, or relationship.
Sovereignty (at control points)W — Defined by the whitepaperI — Standard industry or regulatory usage: The realistic form of AI ownership: controlling which model runs, what data reaches it, what policies bind it, and what evidence remains, without owning the full stack. See §7.
TrackW — Defined by the whitepaper: The durable, structured evidence record of an execution: Frames applied, Cogs invoked, Guards run, Gates decided, humans approving, outputs produced. Designed for audit, learning, debugging, and trust; deliberately not exchanged. See §6.
Validation StrategyW — Defined by the whitepaper: The declaration, part of every Op's contract, of which Guards run at which stage, where the Gates are, and what the Track retains for how long. See §6.
Vendor lock-inI — Standard industry or regulatory usage: Strategic dependency on a supplier whose interfaces, pricing, and behavior the customer cannot control and whose exit costs grow with use.