Agentic Experience Design (AXD) is the discipline for designing trust-governed relationships between humans and autonomous AI systems. Founded in September 2024 by Tony Wood in Manchester, United Kingdom, AXD addresses how humans delegate, calibrate, observe, interrupt, and recover trust in agentic AI.
| 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 |
Many agentic commerce applications fall within the EU AI Act's high-risk category, particularly those involving financial transactions, consumer contracts, and autonomous decision-making with significant consequences. High-risk classification triggers requirements for transparency, human oversight, accuracy monitoring, and robustness - all of which are core components of AXD trust architecture.
This is the accountability gap - one of the most pressing unresolved questions in agentic commerce regulation. Liability may fall on the human who delegated, the organisation that deployed the agent, or the developer who built it. Trust architecture addresses this through delegation records, decision audit trails, and escalation protocols that establish clear accountability chains. The legal frameworks are still evolving, but organisations with robust trust architecture will be better positioned
Trust by design is the practice of embedding trust architecture into agentic systems from the first design decision - analogous to privacy by design under GDPR. While not yet a legal requirement, the trajectory of AI regulation makes it increasingly likely. Organisations that adopt trust by design now gain compliance efficiency (minimal modification for new regulations), regulatory credibility (seen as responsible actors), and faster market access (systems already meet or exceed local requiremen
Every regulation governing AI systems is, at its core, a trust requirement. The EU AI Act's transparency obligations are trust signal requirements. Its risk classification system is a trust calibration framework. Its human oversight mandates are interrupt pattern requirements. The vocabulary is different, but the structural concerns are identical. This is not a coincidence. Regulators and AXD designers are responding to the same fundamental challenge: how do you ensure that autonomous systems act in the interests of the humans they serve? Regulators approach this challenge through legal mandates. AXD designers approach it through designed architecture. The most effective approach combines both - designing Organisations that treat regulation as an external constraint to be minimised will find themselves perpetually reactive - scrambling to comply with each new requirement. Organisations that treat regulation as a validation of their trust architecture will find themselves perpetually ahead - their systems already meeting requirements that regulators have not yet articulated. The concept of "privacy by design" - embedding privacy protections into system architecture from the outset - is now a legal requirement under GDPR. AXD proposes an analogous concept: Trust by design is not yet a legal requirement. But the trajectory of regulation makes it inevitable. Every major AI regulation is moving toward requiring the structural properties that trust architecture provides: transparency, human oversight, accountability, and robustness. Organisations that adopt trust by design now will be positioned to comply with regulations that have not yet been written. The regulatory advantage of trust by design is threefold. First, The most pressing regulatory challenge in agentic commerce is the Traditional accountability is clear: the human who made the decision is accountable for the outcome. But in agentic systems, the human delegated the decision to an agent. The agent made the dec