Engineering Trust in the Age of Autonomous Commerce

 McKinsey says we’re looking at $3-5 trillion in global agentic commerce by 2030. But most enterprises are still designing chatbots while the major players are making significant investments in AI Commerce partnerships and customer experience. 

The Trust Gap

72% of consumers have used AI tools, but only 10% have actually purchased something using AI. So while companies are making strides towards viable agentic commerce, consumers are slow to fully adopt. 

Visa gets it. They’re not just talking about AI—they’re opening their network rails to developers building autonomous agents. Amazon’s “Buy for Me” to make third party purchases off platform, but within the app is powered by Nova and Claude. PayPal and Perplexity’s instant checkout from answer results is shortening conversion funnels.

The hierarchy of trust reveals everything: Consumers trust Apple Pay, PayPal, and Amazon more than their own banks for autonomous transactions. Let that sink in. Financial institutions that have managed money for centuries are being outflanked by tech companies that understand a simple truth—trust in the digital age isn’t about heritage, it’s about experience design, compliance and security.

Key Considerations for Brands Exploring Agentic Commerce

Stop Designing Prompts, Start Designing Goals Traditional UX asks “what does the user want to do?” Agentic design asks “what outcome does the user want achieved?” The difference isn’t semantic—it’s systemic. When agents act on persistent intent rather than reactive queries, the entire interaction model inverts. Success isn’t measured in engagement metrics but in outcomes delivered without intervention.

Progressive Autonomy—The Trust Velocity Framework Forget binary automation. Smart players design four distinct autonomy stages:

• Assistive: Agent responds with prepared options
• Proactive: Agent initiates with confirmation required
• Coordinated: Agent orchestrates multi-step processes
• Autonomous: Agent executes within defined boundaries

Each stage isn’t just a feature toggle—it’s a trust negotiation. The brands that win will be those that can accelerate users through these stages faster than competitors.

Governance as Competitive Moat While startups ship “move fast and break things” AI, enterprises building agentic commerce at scale understand that every autonomous action needs three things: an audit trail, an explanation mechanism, and a ‘kill’ switch. This isn’t compliance theater—it’s infrastructure for a world where one rogue agent decision could tank your stock price.

Orchestration Over Interface The biggest misconception about agentic commerce? That it’s about better chatbots. Wrong. It’s about orchestrating specialized agents—intent interpreters, task decomposers, policy enforcers, execution engines—into symphonies of autonomous action. AI agents working with other agents to complete transactions, safely and securely. The interface is just the tip. The orchestration layer is the iceberg.

The path forward needs to have a design and prototyping process that meets the needs of the consumer, while adhering to compliance standards and regulations.

Define Agent Authority Before Journey Mapping Stop asking “what screens do we need?” Start asking “what decisions can we delegate?” Define goals, authority levels, constraints, and approval requirements before you design a single interaction.

Map Decisions, Not Interactions Traditional journey maps track user actions. Agentic journey maps track decision points, confidence thresholds, explainability moments, and escalation triggers. The experience isn’t what users do—it’s what they don’t have to do.

Prototype Progressive Trust Don’t launch with full autonomy. Launch with value. Test assistive behaviors that save time. Graduate to proactive suggestions that anticipate needs. Only then orchestrate complex multi-step workflows. Trust is earned in increments, not leaps.

Measure Dual Outcomes User metrics: trust velocity, perceived control, value delivered. Operational metrics: deflection rates, straight-through processing, exception handling. If you’re not measuring both, you’re optimizing for failure.