Agent Readiness Index (ARI) is a signal-based framework used to evaluate whether an organization is discoverable, interpretable, and usable by AI agents through its public machine-readable infrastructure.
Rather than measuring brand awareness, market share, or human-facing digital maturity, ARI evaluates whether an entity presents the technical surfaces that autonomous systems need in order to inspect, understand, and interact with it. These surfaces may include elements such as tool registries, OpenAPI specifications, AI plugin manifests, machine-readable endpoints, documentation, dataset exposure, and structured data.
ARI functions as the execution-layer complement to the Entity Clarity Index (ECI).
If ECI measures how clearly an institution is legible to AI systems, ARI measures whether that institution is actionable to them.
In practical terms, ARI reflects a shift in digital strategy:
This distinction matters because AI systems are increasingly moving beyond retrieval and summarization into routing, orchestration, recommendation, and execution. In that environment, organizations with weak machine-readable infrastructure may remain visible to people while becoming structurally invisible to agents.
ARI is typically expressed as a weighted score derived from observable public signals. These signals are not intended to reward hype or AI branding. Instead, they measure whether the underlying infrastructure exists in a form that agents can actually use.
As a strategic concept, ARI highlights a growing divide in the AI era:
That gap will increasingly influence institutional visibility, default inclusion in agentic workflows, interoperability, and long-term relevance.
Read the Agent Readiness Index (ARI) - Tech 100
Read the Lexicon for Entity Clarity Index (ECI)
Read the AI Infrastructure Scarcity Index
Read the Lexicon for Public Agent Surface
Read the Lexicon for Agent-Invisible Entity