--- title: "Just in: What Australia's Trust Gap in Agentic Commerce Means for Brands Selling Through AI Agents" description: "Australian retailers are agentic-ready but shoppers aren't — what this week's biggest agentic commerce stories mean for brands selling through AI agents." date: 2026-09-05 dateModified: 2026-09-05 author: "Malin Gaertig" canonical: https://marksuite.ai/insights/australia-trust-gap-agentic-commerce-brands-selling-through-ai-agents categories: ["Agentic commerce","Market data","Governance"] publisher: MARK (https://marksuite.ai) --- # Just in: What Australia's Trust Gap in Agentic Commerce Means for Brands Selling Through AI Agents *Just in — 2026-09-05* Every week now brings a fresh batch of proof that agentic commerce is moving from slide-deck concept to live infrastructure. This week it was five stories at once: a landmark ad tech ruling, the biggest layoff in a category-leading DSP's history, an acquihire that reveals what platforms actually value in AI, a data point out of Australia that every retailer should sit with, and a payments infrastructure race that's outpacing the shopping experiences it's meant to serve. None of these stories are really separate. Read together, they say something specific about where the next twelve months are headed, and it's not the story most agentic commerce hype has been telling. Every week now brings a fresh batch of proof that agentic commerce is moving from slide-deck concept to live infrastructure. This week it was five stories at once: a landmark ad tech ruling, the biggest layoff in a category-leading DSP's history, an acquihire that reveals what platforms actually value in AI, a data point out of Australia that every retailer should sit with, and a payments infrastructure race that's outpacing the shopping experiences it's meant to serve. None of these stories are really separate. Read together, they say something specific about where the next twelve months are headed, and it's not the story most agentic commerce hype has been telling. ## What did the Google ad tech ruling actually change? On 2 September, a US federal judge declined to force Google to sell off its AdX ad exchange or its DFP publisher ad server, sixteen months after ruling the company had illegally monopolised two ad tech markets. Instead of a break-up, the court ordered behavioural remedies: an end to first look, last look and unified pricing, plus a requirement that Google make its tools interoperable with competitors. For brands, the headline 'no break-up' undersells what happened. The auction mechanics that let Google quietly tilt outcomes in its own favour are being dismantled by court order, and interoperability is now mandatory rather than a talking point in a pitch deck. Publisher yields should firm up over time even without a structural rival emerging. The practical move for any brand running meaningful programmatic spend is to ask your trading desk for the transparency reporting this ruling now requires, rather than waiting for the ecosystem to reshape itself. ## Why did Trade Desk just cut 15 per cent of its workforce? The Trade Desk announced its largest layoff ever this week, cutting roughly 585 roles after its slowest revenue growth since the early pandemic. CEO Jeff Green called it a reorganisation into smaller 'pods,' not a sign of distress — but the timing matters. Independent adtech is being squeezed from two directions simultaneously: retail media and walled gardens taking share on one side, agent-native buying platforms emerging on the other. If a category leader with this much scale needs a reset, the pressure is structural, not company-specific. It's a genuinely useful prompt for any marketing leader to run the same audit internally: is your DSP and agency stack built for where buying is most plausibly headed, or for where it's been for the last five years? Vendor roadmaps for agent-mediated buying deserve more weight in that conversation than current market share does. ## What does Adobe's Rilo acquisition tell us about where AI investment is really going? Adobe quietly acquired a six-person Bengaluru startup called Rilo this week, absorbing its natural-language workflow-automation technology and shutting down Rilo's standalone product. Rilo let non-technical users describe a business process in plain English and have AI agents assemble and run it end to end — competitor intelligence, content repurposing, lead generation. The pattern is worth naming: big platforms aren't buying flashy generative demos anymore, they're buying small teams that got agents to actually finish a workflow. That's a meaningfully higher bar than a chat wrapper on a foundation model, and it's quietly becoming the bar the whole market is being held to. Next time a vendor pitches you an 'AI agent,' ask what it's automated start to finish, not what it's bolted onto. ## What can Australia's agentic commerce data tell brands everywhere? Adyen's 2026 Australia Retail Report is the story I keep coming back to this week. Every retailer surveyed is using AI in some form, and 95 per cent of enterprise retailers say they understand agentic commerce. Shoppers tell a different story: 64 per cent already use AI to browse or compare products, but only 26 per cent are comfortable letting AI complete a purchase, and just 5 per cent are fully comfortable letting an agent buy independently. Separately, 62 per cent said a payment error damages how they see a retailer altogether. This is the gap that matters more than any adoption statistic: supply has sprinted ahead of trust, and payments — not the cleverness of the agent — are where that trust is won or lost. Any brand piloting agentic checkout should be sequencing the boring parts first: biometric authentication, fraud controls, reliable payment processing. Pushing the agent to buy before that foundation is solid most plausibly costs you the relationship the pilot was meant to build. ## So what should brands actually do this week? Underneath all five stories is the same signal: the infrastructure for agents to discover products, evaluate them and pay for them is being built faster than the trust or the demand that would put it to use. Mastercard's agent payment network now spans more than thirty partners across cards, stablecoins and blockchains, and Olas' agent-to-agent marketplace has logged over fourteen million transactions — real volume, largely invisible to consumers who are still deciding whether they trust an agent to do their weekly shop. That order is unusual for a new commerce channel, and it's an opportunity rather than a problem. The practical takeaway: get your own product data agent-readable now — structured catalogues, machine-readable policies, clean feeds — while consumer adoption is still catching up to the plumbing. When volume does arrive, it will most plausibly route to whoever the rails can already read cleanly, not to whoever reacts fastest once the shift is obvious to everyone. ## Key takeaways - Google's ad tech ruling dismantles auction mechanics like first look, last look and unified pricing by court order and makes interoperability mandatory — ask your trading desk for the transparency reporting this now requires. - Trade Desk's 15 per cent layoff signals structural pressure on independent adtech from retail media, walled gardens and agent-native buying — audit whether your DSP and agency stack is built for where buying is headed, not where it's been. - Adobe's Rilo acquihire shows platforms are buying teams that get agents to finish a workflow end to end, not flashy demos — ask any AI agent vendor what it automates start to finish, not what it's bolted onto. - Adyen's Australia data shows the supply side has sprinted ahead of trust: 64 per cent of shoppers use AI to browse, but only 5 per cent are fully comfortable letting an agent buy independently — sequence payment reliability, fraud and authentication before checkout autonomy. - Get your product data agent-readable now — structured catalogues, machine-readable policies, clean feeds — because when volume arrives it will route to whoever the rails can already read cleanly. Source: https://marksuite.ai/insights/australia-trust-gap-agentic-commerce-brands-selling-through-ai-agents ## See also (related MARK analysis) - [Just in: What Anthropic's Retail Agent Blueprints Mean for Brands Selling Through AI Agents](https://marksuite.ai/insights/anthropic-retail-agent-blueprints-brands-selling-through-ai-agents) - [Just in: What Google's Ad Tech Ruling Means for Brands Betting on Agentic Commerce](https://marksuite.ai/insights/google-ad-tech-ruling-brands-betting-agentic-commerce) - [Just in: What Google's Ad-Tech Ruling Means for Brands Selling Through AI Agents](https://marksuite.ai/insights/google-ad-tech-ruling-brands-selling-through-ai-agents) - [Just in: What Australia's AI Trust Gap Means for Brands Selling Through AI Agents](https://marksuite.ai/insights/australia-ai-trust-gap-brands-selling-through-ai-agents) - [Product suite](https://marksuite.ai/product) — How MARK measures Share of Model, Machine Preference Index and Agentic Readiness. - [Framework guides](https://marksuite.ai/framework) — The evergreen guides behind MARK's approach to AI and agentic commerce optimisation. ## About the publisher This analysis is published by MARK (https://marksuite.ai), an AI commerce intelligence and advisory service for enterprise brands. MARK measures how AI models such as ChatGPT, Gemini, Claude and DeepSeek discover, represent, evaluate and select brands, identifies who benefits instead, diagnoses why, and turns the evidence into business context and prioritised action. Written by Malin Gaertig. ## About the author Malin Gaertig is a key person to speak to about Agentic Commerce and how it impacts a company's commercial growth. Malin founded MARK, an AI commerce intelligence and strategic advisory service that combines proprietary technology with 15 years of experience across growth marketing, digital, ecommerce and transformation. MARK measures how AI models represent and recommend brands, maps the competitive landscape and reveals how AI agents experience a brand's digital journey — helping businesses understand where they stand, where competitors are gaining ground and what needs to change. Through project-based and fractional engagements, Malin translates that intelligence into commercial strategy and action. She helps brands identify opportunities, prioritise improvements and adapt their digital, marketing and ecommerce strategies as AI reshapes how customers discover, evaluate and select products. MARK provides the intelligence. Malin brings the interpretation and strategic direction. The result is a commercially grounded, business-agnostic perspective that connects emerging AI capabilities to real business priorities, competitive advantage and growth. Related MARK pages: https://marksuite.ai/score (AI Commerce Benchmark), https://marksuite.ai/product (AI commerce intelligence and measurement), https://marksuite.ai/framework (framework guides). Cite as: MARK — https://marksuite.ai