Agent Experience Integrity (AXI)

Agent Experience Integrity (AXI) is a measure of whether AI agents acting on behalf of users encounter accurate, navigable, and trustworthy interactions with an entity's public surface. It is the agent-side counterpart to user experience — but the two are not interchangeable.

Human users tolerate ambiguity, fill in missing context, and forgive inconsistent presentation. Agents do not. An agent reads structured data, follows links programmatically, parses schema literally, and discards signals it cannot resolve. When an entity's agent-facing surface is incomplete, contradictory, or buried inside HTML rendered for human consumption, the agent fails silently — recommending another option, retrieving a competitor's information, or returning nothing at all.

AXI fails most often in three places:

The first is schema-rendered surface mismatch. An entity's marketing site may present a coherent identity to a human visitor while exposing fragmented, missing, or contradictory schema to an agent crawling the same URL. The agent operates on the schema layer; what the human sees is irrelevant.

The second is action surface absence. An agent attempting to complete a task — book a service, retrieve a price, validate a credential, hand off to a human — needs a machine-callable path. Entities without an MCP server, an API surface, a structured contact endpoint, or a resolvable tool registry cannot be acted upon by agents, only read about. Read-only entities lose to act-upon entities in the agentic layer.

The third is identity drift across surfaces. When the same entity is named, described, or categorized differently across its own properties, in third-party knowledge graphs, and in the training corpora of frontier models, agents resolve the differences by picking one version and ignoring the others. The version that wins is rarely the version the entity would choose.

AXI sits inside the broader entity clarity framework alongside the Agent Readiness Index (ARI), which scores an entity's overall preparedness for agentic interaction, and the Public Agent Surface, which describes the specific resources an agent can access. ARI is the score; Public Agent Surface is the inventory; AXI is the integrity of the experience that score and inventory produce when an agent actually runs.

In the AI-mediated discovery era, AXI is no longer a downstream concern. It is upstream of valuation. Entities that are agent-invisible — present in human-facing search but absent from agent retrieval — face a structural revaluation as more economic activity routes through agents rather than browsers. Entities with high AXI are encountered, transacted with, and cited; entities with low AXI are summarized, substituted, or skipped.

The practical AXI test is simple to state and rarely passed: if an autonomous agent is given a task that should logically resolve to your entity, does it find you, understand you correctly, and complete the task with you — without human intervention?