Todayagenticux.de
The agent gets stuck.
Product facts are hard to extract.
Policies contradict the cart.
Variants and bundles lack decision rules.
Checkout limits appear too late.
AI agents buy differently than people.
I find the exact point where an AI agent gives up on your shop — and turn it into a path it can finish.
Stephan Lucka · 10+ years product & ecommerce UX
Agent check
Where the purchase breaks in your shop.
1path
analysed
analysed
Agent-readyagenticux.de
The path is verifiable.
Evidence is easy to cite.
Rules stay consistent.
Comparisons show clear criteria.
The purchase can be completed.
What changes when the buying path is clear.
Find the point where the purchase stops.
I test whether an agent can find, verify, compare, and buy without guessing.
*Directional score for one typical path: search, product page, policies, and checkout.
UnclearReadableVerifiableCompletableAgent-ready
Agent-Readiness Score
Agent Journey Mapping01
See exactly where AI agents drop off in your shop.
I test how AI agents move through your shop from search to checkout, then turn the weak spots into a ranked fix list: missing context, missing proof, broken rules, and blocked completion.
Tactic
GPT-4 Action Replay + internal browser telemetry. Output: 12-point heatmap per top category, weighted by revenue risk.
Outputs
Agent Intent Mapping
Shop Crawl Diagnostics
Decision Path Modeling
Task Completion Analysis
Policy Visibility Review
Agent Journey Playbooks
"Agents rarely fail everywhere. Usually one path breaks first: search, product proof, policy trust, or checkout."
Audit note
Agent Journey Mapping
Structured Commerce UX02
Product pages where AI agents don't have to guess.
I turn product, category, comparison, and policy pages into clear evidence surfaces so agents can extract facts directly instead of guessing from persuasive copy.
Tactic
Schema.org ProductGroup + 11 Distinguishing-Fact templates. Validated against Google Rich Results Test + our own agent-crawl pipeline.
Outputs
Product Evidence Architecture
Comparison UX
Schema & Content Requirements
Policy Surface Design
Assistant-Readable Copy
AI Search Readiness
"Good product copy is not always good agent input. Claims need proof, structure, and clear decision rules."
Audit note
Structured Commerce UX
Agent Checkout Readiness03
Checkouts that don't strand AI agents.
I find the moments where agents still need a human: shipping, returns, bundles, accounts, payment, and product rules. Then we make those steps explicit enough to complete.
Tactic
Default mapping across 14 category clusters. Service levels as <select> with semantic labels — works in headless agent browsing without JS interaction.
Outputs
Checkout Friction Audit
Cart & Account Flow Review
Shipping Rule Clarity
Returns & Warranty UX
Payment Path Diagnostics
Agent Completion Dashboards
"Checkout fails when rules appear too late: availability, accounts, payment options, return windows, or bundle limits."
Audit note
Agent Checkout Readiness
Trust & Evaluation Systems04
Trust signals ChatGPT & co. will recommend.
I make reviews, specs, guarantees, pricing, availability, and support claims verifiable so agents can recommend you for the right reasons.
Tactic
Shopify metafields + Liquid templates as single source of truth. CI drift test blocks deploys when PDP, cart, policy, and FAQ diverge.
Outputs
Trust Signal Inventory
Review & Proof Architecture
Availability Messaging
Guarantee Clarity
Support Path Design
Agent Evaluation Tests
"Agents need proof they can verify. Reviews, specs, guarantees, and policies have to tell the same story."
Audit note
Trust & Evaluation Systems
Test. Find. Repair.
One real buying path shows where the agent stops. That exact point gets fixed first.
I test one real buying task across search, PDPs, policies, and checkout.
- 01Pick one buying task that maps to real revenue
- 02Run it end to end, the way an agent would
- 03Record every point where it stalls or guesses
The map
The same shop, read two ways.
Humans get through fine. Agents see a different surface — and stall where proof, rules, or the final step are missing. The gap between the two curves is exactly what costs you agent purchases.
Map my shopHuman UXAgent UX
Illustrative example — your real map comes out of the audit.
Why agenticux.de exists.
agenticux.deThe idea
Why agenticux.de exists.
I am Stephan Lucka. For more than ten years I have worked on product and ecommerce UX. agenticux.de applies that work to AI agents: I check whether a shop gives them the facts, rules, and next steps they need to justify a recommendation and complete a purchase.
- One real buying path.
- One clear breakpoint.
- Fixes your team can check.
Typical breakpoints
"Agents do not drop off because the page looks bad."
They drop off when product facts, policies, comparisons, or checkout rules stop lining up.
Agentic UX checkTypical breakpoint
No redesign on suspicion.
First the drop-off. Then the targeted fix.
ExamplePath analysis
Why agents stop during selection.
JourneyProduct selection
RiskMissing evidence
OutputFix roadmap
4
breakpoints checked12
possible fixes mappedUsed:
Agent journey auditProduct evidenceCheckout rules+2
"When facts, variants, and rules are scattered, an agent cannot make a confident recommendation."
AnalysisPath analysis
Path analysisCheck first. Then build.
Start with one path.
The first 30 minutes show where an agent drops off and whether a deeper audit is worth it.
Stephan Lucka
Agentic UX Consultant
From
€2,500/month
In 10 days you know exactly where AI agents drop off in your shop — and which three fixes move the most revenue.
List of 12 decision hotspots, weighted by revenue risk
Checkout risk report with revenue impact per fix
AI search coverage against ChatGPT, Perplexity, Claude
Schema audit for Product, Offer & Policy
30-day check-in after going live
FAQ
I test one buying task from an AI agent's perspective and show where the purchase becomes uncertain.
Insights.
Field notes from working with AI agents in real shops — and what you can do before your competitors read it.
Agentic Commerce15 min read
May 5, 2026
Agentic UX: why shops need to be readable for agents
A practical, SEO and AEO optimized guide to agentic UX for ecommerce teams: what changes when AI agents parse, compare, trust, and complete purchases.
Read article
AEO15 min read
May 5, 2026
AEO for shops: become the source AI systems can cite
A practical AEO guide for ecommerce teams: how to structure pages, evidence, schema, and internal answers so AI search systems and shopping agents can cite your shop.
Read article
Let’s find the first drop-off.
Agent-readiness diagnostic of your shop
Three prioritized fixes by revenue impact
Concrete next steps within 24h
"Send me the shop and one product path. I will show where the agent stops and which fix should be checked first."
First diagnosticPractical answer within 24 hoursagenticux.de
Send me the shop and one product path. I will check where the agent gets stuck.
1buying path checked.
24huntil the first assessment.