Ora is a fundamental infrastructure layer for the agentic web. By defining and measuring "Agent Experience" (AX), they provide the first major benchmarking system for how well businesses can be navigated by autonomous LLMs. Their work is directly relevant to anyone building agents, as it identifies which companies have the APIs, auth protocols, and documentation necessary for an agent to succeed.
In particular, Ora is a champion of the Model Context Protocol (MCP) and llms.txt, pushing these as the new standards for agent discovery and interaction. They matter because they are moving the conversation from "AI-powered features" to "agent-accessible architecture," grading the world's APIs on their ability to support the coming wave of autonomous machine users.
Ora is a technical benchmarking platform that standardizes a concept it calls Agent Experience (AX). For the last twenty years, businesses focused on UX—optimizing digital products for human eyes and manual clicks. Ora argues this model is increasingly irrelevant. As AI agents become the primary way users interact with the web, businesses must instead optimize for machine agents that reason, parse structured data, and call APIs. If an agent hits an infrastructure wall, it simply moves on to a more accessible competitor.
Based in the United States, Ora provides an Agent Readiness Ranking that evaluates how "agent-ready" a business is across five specific layers: discovery, identity, auth and access, agent integration, and user experience. The platform uses a proprietary scoring methodology called Deep Scan v2 to simulate real-world agent behavior. Rather than relying on static checklists, Ora spawns live agents across platforms like ChatGPT, Claude, and OpenClaw to attempt end-to-end task execution on a company’s site.
The weighting of Ora's readiness score reflects the practical barriers to agent autonomy. Auth and access are ranked as the most critical layers. The logic is simple: if an agent can find a service but cannot authenticate or get past a human-centric login gate, the journey stops. Identity and integration follow; agents need to know exactly what a product does and have the necessary APIs, SDKs, or Model Context Protocol (MCP) servers to execute actions.
Discovery and user experience form the bookends of the ranking. While being findable via files like llms.txt is considered table stakes, the system also accounts for the "human handoff." Even autonomous agents eventually need to surface a UI for high-stakes decisions like final payments or legal confirmations. A high AX score indicates a business has successfully minimized friction for both the agent and the eventual human supervisor.
The platform's technical core is its automated lab environment. When a domain is scanned, Ora runs static checks against public documentation and registries. This is followed by the live simulation where agents attempt to onboard and use the product. This approach allows the system to reverse-engineer its scores based on where agents actually succeed or stall. The current iteration of the scan completes in under 30 seconds, providing immediate feedback on a company's technical accessibility.
Ora was founded by technical leaders who previously worked with OpenAI and Anthropic to establish early standards for interactive AI. The company frames its work as the necessary infrastructure for a world of autonomous agents. By ranking over 12,000 sites, Ora provides a public leaderboard that includes categories like CRM, Fintech, and Developer Tools. These rankings help developers understand where their infrastructure lacks the clarity or access required for modern LLMs to operate effectively.
A ranking system evaluating how ready a business is for AI agents across five technical layers.
Ora is hiring.