Embedding taxonomy

LICENSE

@@ -1,4 +1,4 @@
-User Context Protocol (UCP)
+Agentic Audiences
 Copyright (c) 2025 LiveRamp Holdings, Inc.
 
 ======================================================================
@@ -15,11 +15,6 @@ This repository contains two types of materials, each with its own license:
    Licensed under: Apache License 2.0
    See: https://www.apache.org/licenses/LICENSE-2.0
 
-Trademarks:
-"User Context Protocol" is a trademark of LiveRamp Holdings, Inc.
-Use of these marks is subject to the terms described in TRADEMARK.md and is not
-granted by either license above.
-
 ======================================================================
 LICENSE DETAILS
 ======================================================================
@@ -31,7 +26,7 @@ A. Specification and Documentation License (CC BY 4.0)
 Files covered:
 - All Markdown files under /specs and /docs
 - All schema and example files (.json, .yaml, .yml, .md) in the specification tree
-- Any whitepapers, diagrams, and explanatory materials describing UCP
+- Any whitepapers, diagrams, and explanatory materials describing Agentic Audiences
 
 Summary:
 You are free to share and adapt the specification for any purpose, provided
@@ -41,7 +36,7 @@ Full license text available at:
 https://creativecommons.org/licenses/by/4.0/legalcode.txt
 
 Example attribution:
-"Portions of this work are based on the User Context Protocol,
+"Portions of this work are based on Agentic Audiences,
 developed by LiveRamp Holdings, Inc. (CC BY 4.0)."
 
 ----------------------------------------------------------------------
@@ -71,7 +66,7 @@ C. Contact
 For legal inquiries or permission requests, contact:
 
 LiveRamp Holdings, Inc.
-Attn: Legal Department – User Context Protocol
+Attn: Legal Department – Agentic Audiences
 225 Bush Street, 17th Floor
 San Francisco, CA 94104
 Email: legal@liveramp.com

README.md

@@ -1,16 +1,18 @@
-# User Context Protocol™ (UCP)
+# Agentic Audiences
 
 An Open Protocol for Intelligent Interoperability Across Advertising Agents
 
+> **Note:** Agentic Audiences was formerly known as the User Context Protocol (UCP).
+
 > **Note:** This specification represents LiveRamp's initial proposal. We have open-sourced this repository to enable the community to collaboratively define and reach collective agreement on a standard for embedding exchange in agentic advertising.
 
 ---
 
 ## Overview
 
-The User Context Protocol (UCP) is an open standard proposed by LiveRamp to enable intelligent agents in advertising and marketing to interoperate through the exchange of **signals**—identity, contextual, and reinforcement information—that represent a consumer's true real-time intent and response to advertising.
+Agentic Audiences is an open standard proposed by LiveRamp to enable intelligent agents in advertising and marketing to interoperate through the exchange of **signals**—identity, contextual, and reinforcement information—that represent a consumer's true real-time intent and response to advertising.
 
-As the industry transitions into the agentic web, where autonomous buyer, seller, and measurement agents powered by AI/ML models act on behalf of users and organizations, advertising decisions increasingly rely on these models to process billions of signals per second. UCP defines a protocol for agents to exchange **embeddings**—compact, learned vector representations that efficiently encode identity signals (who the user is), contextual signals (what they're doing right now), and reinforcement signals (how they respond to ads) in a privacy-preserving, interoperable format.
+As the industry transitions into the agentic web, where autonomous buyer, seller, and measurement agents powered by AI/ML models act on behalf of users and organizations, advertising decisions increasingly rely on these models to process billions of signals per second. Agentic Audiences defines a protocol for agents to exchange **embeddings**—compact, learned vector representations that efficiently encode identity signals (who the user is), contextual signals (what they're doing right now), and reinforcement signals (how they respond to ads) in a privacy-preserving, interoperable format.
 
 This repository contains:
 - **Technical specifications** for embedding exchange formats and schemas
@@ -45,7 +47,7 @@ Today's advertising systems struggle to efficiently exchange these signals:
 - **Support real-time inference**: Fast vector operations enable sub-100ms decisions
 - **Unify signal types**: A single embedding can simultaneously encode who the user is, what they're doing, and how they've responded to past interactions
 
-UCP defines how agents exchange these embeddings, transforming advertising from prompt-driven coordination to embedding-based interoperability that spans the entire decision-feedback loop.
+Agentic Audiences defines how agents exchange these embeddings, transforming advertising from prompt-driven coordination to embedding-based interoperability that spans the entire decision-feedback loop.
 
 1. **Phase 1 – Agent Interoperability Layer**  
    Enable existing LLM agents to exchange structured marketing context using standardized inputs and outputs.
@@ -60,7 +62,7 @@ UCP defines how agents exchange these embeddings, transforming advertising from
    These embeddings act as transferable memory between agents that share a compatible vector space, enabling near real-time optimization without large prompt contexts.
 
 > **📄 Deep Dive: AI/ML Models in Agentic Advertising**
-> The [`/docs/AI_ML Models in Agentic Digital Advertising Era.pdf`](docs/AI_ML%20Models%20in%20Agentic%20Digital%20Advertising%20Era.pdf) whitepaper provides comprehensive coverage of the 15+ model categories—from Audience Discovery and Lifetime Value Prediction to Multi-Touch Attribution and Incrementality Measurement—that power agentic advertising systems. These models both **consume** embeddings (using them as input features) and **produce** embeddings (generating vector representations of users, contexts, and creatives) that are exchanged via UCP. Understanding this model ecosystem is essential for implementing UCP-compatible agents.
+> The [`/docs/AI_ML Models in Agentic Digital Advertising Era.pdf`](docs/AI_ML%20Models%20in%20Agentic%20Digital%20Advertising%20Era.pdf) whitepaper provides comprehensive coverage of the 15+ model categories—from Audience Discovery and Lifetime Value Prediction to Multi-Touch Attribution and Incrementality Measurement—that power agentic advertising systems. These models both **consume** embeddings (using them as input features) and **produce** embeddings (generating vector representations of users, contexts, and creatives) that are exchanged via Agentic Audiences. Understanding this model ecosystem is essential for implementing Agentic Audiences-compatible agents.
 
 ---
 
@@ -76,35 +78,35 @@ UCP defines how agents exchange these embeddings, transforming advertising from
 
 ## Agent Ecosystem
 
-UCP builds on and extends the [**Ad Context Protocol (ADCP)**](https://github.com/adcontextprotocol/adcp), an open standard for advertising automation that enables AI assistants to manage campaigns through natural language interactions.
+Agentic Audiences builds on and extends the [**Ad Context Protocol (ADCP)**](https://github.com/adcontextprotocol/adcp), an open standard for advertising automation that enables AI assistants to manage campaigns through natural language interactions.
 
-**How UCP Complements ADCP:**
+**How Agentic Audiences Complements ADCP:**
 
 - **ADCP** defines the control plane—how agents interact with advertising platforms (Signals Activation, Media Buy, Curation protocols)
-- **UCP** defines the data plane—how agents exchange embeddings that encode identity, contextual, and reinforcement signals
+- **Agentic Audiences** defines the data plane—how agents exchange embeddings that encode identity, contextual, and reinforcement signals
 
 Together, these protocols enable a complete agentic advertising ecosystem:
 
 | Layer | Protocol | Purpose |
 |-------|----------|---------|
 | **Control** | ADCP | Agent commands and platform integrations (activate audiences, execute buys, manage inventory) |
-| **Data** | UCP | Agent-to-agent embedding exchange (share learned representations of users, contexts, and outcomes) |
+| **Data** | Agentic Audiences | Agent-to-agent embedding exchange (share learned representations of users, contexts, and outcomes) |
 
 **Example Integration:**
 1. A buyer agent uses **ADCP** to discover audience signals: "Find premium sports enthusiasts interested in running shoes"
 2. The platform returns data via **ADCP's Signals Activation Protocol**
-3. The buyer agent uses **UCP** to exchange contextual and identity embeddings with a seller agent
+3. The buyer agent uses **Agentic Audiences** to exchange contextual and identity embeddings with a seller agent
 4. The seller agent uses embeddings to match inventory in real-time via vector similarity
-5. Reinforcement signals (impressions, conversions) flow back through **UCP** to update models
-6. The measurement agent uses **ADCP** to report results and **UCP** to share learned embeddings
+5. Reinforcement signals (impressions, conversions) flow back through **Agentic Audiences** to update models
+6. The measurement agent uses **ADCP** to report results and **Agentic Audiences** to share learned embeddings
 
-By integrating with ADCP's agent ecosystem, UCP enables the transition from prompt-based advertising automation to embedding-based intelligence to drive efficiencies by eliminating the need for massive copies of user-level datasets across the ecosystem.
+By integrating with ADCP's agent ecosystem, Agentic Audiences enables the transition from prompt-based advertising automation to embedding-based intelligence to drive efficiencies by eliminating the need for massive copies of user-level datasets across the ecosystem.
 
 ---
 
 ## Technical Vision
 
-UCP defines:
+Agentic Audiences defines:
 
 1. **Protocol Interfaces** - APIs and schemas for exchanging context, signals, and results.
 2. **Context Management** - Strategies for maintaining scoped, composable context windows in LLM-driven agents.
@@ -114,7 +116,7 @@ UCP defines:
 6. **Agentic Attestation** - Ensures confidentiality and integrity of code and information accessed or executed through agents, including provenance and controlled execution environments.
 7. **Token Exchange and Settlement** - Enables agents to exchange tokens or perform value transfers for advertising events, supporting integration with emerging payment and attribution protocols such as AP2 and X402.
 
-By evolving from structured text exchanges to compact vector exchanges, UCP will enable major gains in speed, scale, and cost efficiency for campaign optimization.
+By evolving from structured text exchanges to compact vector exchanges, Agentic Audiences will enable major gains in speed, scale, and cost efficiency for campaign optimization.
 
 ---
 
@@ -123,7 +125,7 @@ By evolving from structured text exchanges to compact vector exchanges, UCP will
 1. **Today:**  
    - A buyer agent prompts a seller agent:  
      "Provide available CTV inventory for users interested in electric vehicles in San Francisco this week."  
-   - The seller agent responds using the UCP schema, returning JSON data on available segments.
+   - The seller agent responds using the Agentic Audiences schema, returning JSON data on available segments.
    - A measurement agent records conversions and feeds updates.
 
 2. **Future:**  
@@ -145,11 +147,11 @@ By evolving from structured text exchanges to compact vector exchanges, UCP will
 
 ## Contributing
 
-This repository hosts the evolving UCP specification and reference implementations.
+This repository hosts the evolving Agentic Audiences specification and reference implementations.
 We welcome contributions from engineers, researchers, and organizations shaping the next generation of agentic advertising.
 
 To get involved:
-- Read [`/docs/AI_ML Models in Agentic Digital Advertising Era.pdf`](docs/AI_ML%20Models%20in%20Agentic%20Digital%20Advertising%20Era.pdf) to understand the model ecosystem that UCP enables
+- Read [`/docs/AI_ML Models in Agentic Digital Advertising Era.pdf`](docs/AI_ML%20Models%20in%20Agentic%20Digital%20Advertising%20Era.pdf) to understand the model ecosystem that Agentic Audiences enables
 - Fork the repo and explore the `/specs` directory for technical specifications
 - Propose changes via pull request
 - Join or start a working group under `/community`

specs/v1.0/README.md → specs/v1.0/embedding-exchange.md

@@ -1,7 +1,7 @@
-# UCP Contextual Embedding Exchange Specification (Draft v0.1)
+# Agentic Audiences Contextual Embedding Exchange Specification (Draft v0.1)
 
 Status: Draft  
-Scope: Defines a vendor-neutral wire format for exchanging contextual embeddings between agents in the User ContextProtocol (UCP) ecosystem.  
+Scope: Defines a vendor-neutral wire format for exchanging contextual embeddings between agents in the Agentic Audiences ecosystem.  
 Primary transport: HTTPS JSON (optionally NDJSON for streaming). Binary variants MAY use CBOR with identical field names.
 
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