Tony Wood examines agent legibility - making autonomous AI systems structurally readable to humans and other agents. The bridge between Signal Clarity and trust architecture..
| 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 |
Agent legibility is the agent-side complement to Signal Clarity. Signal Clarity (the first pillar of AXD Readiness) addresses how merchants make their products and services readable to agents. Agent legibility addresses how agents make themselves readable to humans and to other agents. Together, they form the bidirectional communication infrastructure of agentic commerce - the system by which agents understand merchants and humans understand agents.
The three layers of agent legibility are: (1) Identity legibility - the agent declares what it is, who it represents, what authority it holds, and what constraints govern its behaviour. (2) Process legibility - the agent makes its decision-making process structurally readable, so that observers can understand not just what it decided but how and why. (3) Outcome legibility - the agent provides clear, comprehensible accounts of what it did, what resulted, and how the outcome relates to the origin
Agent legibility is the agent-side complement to Signal Clarity. Signal Clarity (the first pillar of AXD Readiness) addresses how merchants make their products and services readable to agents. Agent legibility addresses how agents make themselves readable to humans and to other agents. Together, they form the bidirectional communication infrastructure of agentic commerce - the system by which agents understand merchants and humans understand agents.
The three layers of agent legibility are: (1) Identity legibility - the agent declares what it is, who it represents, what authority it holds, and what constraints govern its behaviour. (2) Process legibility - the agent makes its decision-making process structurally readable, so that observers can understand not just what it decided but how and why. (3) Outcome legibility - the agent provides clear, comprehensible accounts of what it did, what resulted, and how the outcome relates to the origin
The most dangerous AI agent is not the one that makes bad decisions. It is the one whose decisions cannot be read. An agent that makes a poor choice within a legible framework can be corrected, constrained, or redirected. An agent whose reasoning is opaque - whose identity is unclear, whose decision process is invisible, whose outcomes are reported without context - cannot be trusted, cannot be calibrated, and cannot be governed. This is the legibility crisis of the agentic age, and it is the subject of this essay. The term "agent legibility" is emerging in practitioner discourse as the operational expression of what the AXD Institute has called Signal Clarity from the agent's perspective. This essay examines what agent legibility actually means, why it matters for The current generation of AI agents operates in a condition of profound illegibility. When a customer delegates a task to an AI shopping agent, they typically have no structured way to understand what the agent is, who built it, what data it accesses, what constraints govern its behaviour, how it makes decisions, or how it will report its outcomes. The agent is a black box that accepts instructions and returns results. The space between instruction and result - the space where all the consequential decisions happen - is invisible. This illegibility is not a technical limitation. It is a design failure. The technology to make agents legible already exists: structured identity declarations, decision audit trails, outcome reporting frameworks, capability manifests. What does not yet exist is the design discipline that specifies how these technical capabilities should be composed into a coherent legibility architecture. This is the gap that The legibility crisis is not merely an inconvenience. It is a structural barrier to the adoption of agentic commerce. Research consistently shows that the primary obstacle to AI agent adoption is not capability but trust. Customers are willing to delegate to agents that th