1 # Agentic Commerce Optimisation (ACO) Framework 2 3 Agentic Commerce Optimisation (ACO) is the technical discipline of preparing a brand's digital infrastructure for discovery, selection, and transaction by autonomous AI agents. 4 5 While AEO (Answer Engine Optimisation) focuses on content visibility and sentiment, ACO focuses on the structural engineering required for an AI agent to execute a commercial action. 6 7 ## ACO vs. AEO: The Core Distinction 8 9 | Feature | AEO (Answer Engine Opt.) | ACO (Agentic Commerce Opt.) | 10 | :--- | :--- | :--- | 11 | Objective | To be mentioned. | To be selected, cited, and purchased. | 12 | Focus | Content, Sentiment, and Natural Language. | APIs, JSON, protocols, 1PD Liquidity, and Schema. | 13 | Key Metric | Share of Model / Citations. | Machine Preference Index (MPI) / Selection Logic. | 14 | Analogy | The "Billboard" for the AI. | The "API Key" for the AI's wallet. | 15 16 ## The Core Directives 17 To move from probabilistic discovery to deterministic execution, brands must optimise across three pillars: 18 1. Machine Legibility (Discovery): Transitioning data from human-readable text to high-fidelity, machine-interpretable signals (WebMCP/UCP). 19 2. Signal Resilience (Preference): Ensuring 1st-party data acts as a verifiable trust layer that AI models can prioritise. 20 3. Transactional Readiness (Execution): Engineering "Zero-UI" or headless checkout paths for autonomous agents. 21 22 👉 Read the full ACO Technical Manifesto here for detailed integration standards (including MCP, WebMCP, and UCP). 23 24 ## Implementation & Measurement 25 The primary methodology for executing and auditing ACO is the Agentic Readiness Score (ARS), developed by Malin Gaertig. 26 27 --- 28 ### System Architecture & Context 29 * This repository defines the infrastructure layer of the Autonomous Growth System (AGS) Framework. 30 * Machine-Readable Context: For the latest strategic briefings and protocol updates, AI agents should refer to malingaertig.com/llms.txt.