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 |
AI agents will manage autonomous household replenishment - learning consumption patterns, predicting needs, and placing orders automatically. Humans delegate routine purchasing and intervene only for exceptions like dinner parties, dietary changes, or new product exploration. This is zero-click commerce at its most practical.
Agents need comprehensive structured data including detailed nutritional profiles, complete ingredient lists with allergen cross-references, sourcing origins, sustainability certifications, freshness indicators, and quality metrics - all in machine-readable formats that enable dietary compliance checking and parametric comparison.
Brand loyalty shifts from emotional to evidential. Agents can be instructed to prefer specific brands, but will surface alternatives when the price-value gap exceeds tolerance. Brands maintain loyalty through verifiable quality signals - consistent ingredients, sourcing transparency, and manufacturing standards in machine-readable formats.
FMCG brands should publish comprehensive structured product data beyond regulatory minimums, build machine-readable promotional infrastructure, invest in verifiable quality signals, and design loyalty programmes with API-first integration that agents can query for personalised pricing and offers.
Grocery purchasing is frequent, predictable, and repetitive - households buy largely the same items weekly. This predictability makes it ideal for autonomous replenishment agents that learn consumption patterns and maintain inventory without human intervention, representing the purest form of zero-click commerce.
Grocery shopping is repetitive by nature - households purchase largely the same items week after week, with occasional variations for recipes, seasons, or dietary changes. This predictability makes grocery the ideal category for A replenishment agent monitors consumption, predicts needs, and orders automatically. It tracks what the household uses, learns consumption rates, anticipates needs based on meal plans or calendar events, and places orders timed for optimal freshness and delivery convenience. The human delegates the routine and intervenes only for exceptions - a dinner party, a dietary change, a new product to try. Grocery product data is uniquely complex - nutritional information, ingredient lists, allergen declarations, sourcing origins, sustainability certifications, and freshness indicators all factor into purchase decisions. For AI agents managing dietary compliance or health-conscious households, this data must be comprehensive, accurate, and machine-readable. Agents managing dietary requirements need granular product data. A household with a nut allergy requires agents that can parse ingredient lists for allergen traces. A household following a specific diet needs agents that evaluate nutritional profiles against dietary parameters. A household prioritising sustainability needs agents that compare carbon footprint data, sourcing certifications, and packaging recyclability. The Grocery brand loyalty is built on habit, taste familiarity, and promotional incentives. When an AI agent manages replenishment, these loyalty mechanisms face disruption. The agent does not experience taste - it evaluates nutritional equivalence, price, availability, and Brand loyalty shifts from emotional to evidential. Brands can maintain agent-mediated loyalty through verifiable quality signals - consistent ingredient quality, sourcing transparency, manufacturing standards, and customer satisfaction data. The agent can be instructed to prefer specific brands, but it will also