Machine-Readable Commerce: Making Products Discoverable by AI Agents — an AXD Institute resource on agentic experience design, agentic commerce, trust architecture, and human agent interaction. Founded by Tony Wood..
| Dimension | Traditional UX | Agentic Experience Design (AXD) |
|---|---|---|
| Primary material | Attention and affordance | Trust and delegation |
| User state | Present, navigating | Absent, delegating |
| Design output | Screens and interfaces | Outcomes and constraints |
| Temporal model | Session-based | Relationship-based |
| Success metric | Task completion | Trust calibration |
Machine-readable commerce is the practice of structuring all commercial information - products, prices, availability, policies, reputation, and transaction capabilities - in formats that autonomous AI agents can discover, parse, compare, and act upon without human interpretation. It is the technical foundation of the Signal Clarity pillar in the AXD Readiness framework and a prerequisite for participating in the agentic economy.
In the agentic economy, autonomous AI agents increasingly mediate purchasing decisions. An agent evaluating suppliers compares structured data - not marketing copy. A business that is not machine-readable is invisible to these agents, regardless of its brand strength or marketing spend. Machine readability is the minimum viable requirement for being discovered, evaluated, and selected by autonomous shopping agents.
AI agents need comprehensive Schema.org Product markup (name, price, availability, reviews, parametric attributes), programmatic API access to product catalogues, machine-readable business policies (returns, shipping, warranties), and verifiable trust signals (uptime metrics, SLA compliance, delivery accuracy). The Universal Commerce Protocol (UCP) provides a standardised manifest format for declaring all commerce capabilities to agents.
Test from the agent's perspective using agent simulation tools, Schema.org validators, and API testing frameworks. Monitor agent traffic separately from human traffic. Measure structured data coverage, API response times, and agent conversion rates. Use the AXD Readiness Maturity Model to benchmark progress across four levels from unready to optimised.
Machine readability is the technical implementation of the Signal Clarity pillar - the first of the Four Pillars of AXD Readiness. Signal Clarity measures whether a business's products and services are discoverable and evaluable by autonomous agents. Machine-readable commerce provides the structured data, APIs, and formats that make Signal Clarity achievable. Without machine readability, the other three pillars cannot function.
Create machine-readable warranty specifications. Include warranty duration, coverage scope, claim process, and exclusions as structured data. In B2B Publish dispute resolution processes in structured formats. Agents acting on behalf of consumers need to evaluate what happens when things go wrong - not just what happens when things go right. Machine-readable dispute resolution data is a