Designing Agent-Ready Websites for AI Web Agents: A Framework for Machine Readability, Actionability, and Decision Reliability

Short summary

This paper introduces a design framework for agent-ready websites that enhance readability, actionability, and decision reliability for AI browser agents on e-commerce platforms. In a controlled experiment with 300 runs across three browser-agent models, agent-ready sites achieved 89.3% strict success versus 49.3% for human-oriented baselines, with large gains in product extraction, comparison, and multi-constraint selection. The framework covers agent interpretability, executability, and decision reliability through structural clarity, action cues, and temporal validity indicators.

• •Agent-ready website framework improves AI browser-agent success from 49.3% to 89.3% in controlled e-commerce experiments
• •Three dimensions: agent interpretability, agent executability, and agent decision reliability
• •Tested across GPT-4.1, Gemini-2.5 Flash, and Grok-4 Fast with 300 runs measuring pass/fail, step counts, and token consumption

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