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AI agents are already disrupting how consumers discover, evaluate, and buy, but the trust gap is the next barrier to scale. Consumers want to know their data is protected, their transactions are secure, and that an AI agent acting on their behalf is actually acting in their interest. This is especially true when agents begin to act independently in agentic commerce, whether they’re sharing data, applying preferences, or completing transactions without direct user interaction. The brands that close the trust gap first—by working across commercial and security teams to address fraud, consent, and data protection from the start—won't just avoid risk. They’ll build the kind of loyalty that translates directly into long-term growth and competitive advantage.
Agentic commerce can’t scale unless safety is part of the design.
Can consumers trust your AI agents?
People are increasingly using AI to find products and services, especially millennials, Gen Z, and even kids in Generation Alpha, but many still aren't comfortable letting an AI agent complete the purchase. They want to know what they're getting in exchange for their data, what they're consenting to, what decision an AI agent is authorized to make on their behalf, and what protections are in place if something goes wrong.
Companies that offer this level of transparency and control, instead of so-called “black box AI,” are likely to boost both sales and long-term loyalty. Embedding these controls from the start is also far more efficient and far less costly than retrofitting them later. But most companies haven’t upgraded to meet the rise of agentic commerce.
Four actions to promote trust in agentic commerce
The brands that are most likely to lead in the future are the ones that deploy agents confidently and at scale in ways their customers can see and verify. That starts with commercial and security teams working together, not in sequence, from the very beginning.
- Provide clear notice and consent options, and be transparent about data usage. Inventory and assess your agentic AI use cases end to end. Document data inputs, consent requirements, contractual obligations, and guidelines for agents' activities and modify them where needed. Then make it easy for customers to understand how their data is being used, what they are gaining by consenting, and how transaction data will inform the system going forward. Build platforms that allow users to grant, manage, and revoke permissions for AI agents acting on their behalf, including access to financial data, and provide mechanisms that explain how agents operate and how their decisions affect consumers. This isn’t just about compliance. It’s about defining and enforcing the boundaries of what an AI agent is allowed to do on a customer’s behalf.
Establish a data taxonomy and implement automated metadata tagging to flag and segregate youth data from general user data, enabling cleaner compliance workflows and more reliable deletion processes. Build retention, deletion, and archival controls designed for the continuous data streams that agentic systems generate, including derived preferences, behavioral logs, and records of delegated authority. Limit third-party access to sensitive data by design, and clearly document what is shared, with whom, and for what purpose. This matters especially as Gen Alpha enters the picture. Protecting minors is no longer just a reputational consideration. Youth data protection, increasingly reflected in evolving regulations, will shape not just compliance requirements, but also consumer expectations for control and transparency.
- Strengthen boundaries. Design data clean room capabilities and implement controls that limit third party access to sensitive data. Track what data is shared with third parties, for what purpose, and establish procedures to regularly recertify that access, including requesting deletion from third-party systems where applicable. Ensure sensitive data is de-identified through anonymization, pseudonymization, or tokenization wherever possible to reduce exposure and limit downstream risk.
Adopt leading practices such as payment card industry (PCI) security standards, zero trust architecture—including zero trust principles for agent-to-agent communication—and machine identity segmentation to continuously verify access to data and control agent privileges. Secure your API design and microservices architecture and implement identity and access management across both human and machine identities to prevent lateral movement in the event of a compromise. These aren't just technical measures—they're the infrastructure of consumer confidence, and the foundation on which operational resilience depends.
- Secure the decision layer. This is where commercial teams need their security colleagues most. Agentic systems introduce failure modes that traditional security architectures weren't built to handle: model drift, hallucinations, adversarial prompt injection, and API misconfigurations that can expose payment credentials or manipulate order logic. A malicious actor doesn't need to breach your perimeter. They may only need to manipulate what your agent believes it has been asked to do. In agentic commerce, the decision layer becomes the experience layer, making its integrity directly visible to consumers.
Leading practices to counter this include red-teaming agents’ logic by simulating adversarial prompts and attack scenarios, establishing AI-specific incident response and resilience playbooks, and using large language model (LLM) observability mechanisms to track potential prompt abuse, model drift, and emerging risk signals before they become customer-facing failures. This level of visibility isn’t just a control. It’s quickly becoming a customer expectation. Implement resilience planning, backup policies, and recovery time and point objectives aligned to agentic-specific system failure modes.
Making these protections visible matters too. Real-time fraud alerts and user-facing audit trails give customers concrete evidence that you're looking out for them. Disclose how agent-generated data is used for model training and continuous improvement, including any impacts on model behavior and risk posture. In a market full of vague "privacy-first" claims, that kind of specificity is itself a source of competitive advantage. - Test before you launch and keep watching after you do. Deploying an agent is not a one-time event. Consumers and regulators are beginning to want more than assurance. They want evidence. The retailers and brands that earn lasting trust will be the ones willing to show their work publicly.
That means publishing the results of pre-deployment testing. Confirm that specific guardrails are in place and share evidence they perform as intended before your agent ever reaches a customer. Post-launch, give consumers and stakeholders visibility into how your systems are monitored: what you're watching for, how often, and what happens when something flags. Disclose clearly how agent-generated data—including behavioral logs and interaction histories—feed back into model training, and what governance controls ensure that process doesn't introduce new risk.
Retailers that get ahead of this won't just avoid regulatory scrutiny. They'll own a meaningful point of differentiation at a moment when most of their competitors are still figuring out what their agents are doing.
Making safety a source of growth
As agentic commerce continues to accelerate rapidly, brands that embed trust, privacy, and consumer empowerment into each layer of the experience stand to earn something increasingly rare: consumers' confidence. In a crowded and fast-moving market, that confidence can accelerate adoption, deepen loyalty, and become a defining competitive advantage.
Rethinking agentic commerce
Prioritizing safety for what’s next
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