Retailers are facing a watershed moment in artificial intelligence development. Just as direct-to-consumer brands have begun to draft and implement solid policies, governance systems and approval thresholds for generative AI use cases, agentic AI systems are already out of the barn. Predictive, knowledge-based LLM systems have paved the way for systems that don’t just talk, they do. For example, a chatbot generates in response to prompt inputs. An agent is like a colleague that must be supervised and trained, but can act independently and even go rogue.

Agentic AI systems can move beyond prompt-response generation to take specific, autonomous action based on uncircumscribed datasets. They can proactively navigate digital environments, potentially being the first layer in a user’s interaction with a retailer’s website or app. These tools provide enormous opportunity for retailers, which can now train and deploy AI agents to complete tasks such as dynamically adjusting pricing, handling start-to-end purchasing processes, generating campaigns using autonomous research and customer listening, and streamlining inventory and supply chain issues in real time.

That said, while these systems promise efficiency and scale, they also introduce new and heightened categories of legal risk. When AI systems are empowered to decide and act — not just making predictions but using their own “judgment” to solve problems — traditional compliance frameworks and internal approval thresholds may no longer be sufficient to limit legal exposure before things spiral out of control.

Here are the top 10 legal risks retailer leaders should address through proactive governance now as agentic AI becomes swiftly embedded across commerce, marketing, and operations.

1. Deceptive and Misleading Consumer Communications

Agentic AI can move beyond generating marketing copy, dynamically adapting messaging and conducting its own review of claim substantiation to develop and even disseminate product claims. However, these capabilities amplify existing false advertising risks when AI systems personalize and publish claims, promotions, or product descriptions in ways that are unsubstantiated, inconsistent, or misleading. Unlike static marketing content, these outputs can change continuously, making traditional pre‑review impossible and exposing retailers to liability under state and federal consumer protection laws. Furthermore, if an AI tool is used to automatically disseminate press releases or crisis response, things can quickly spiral out of control from a reputational perspective if the tool hasn’t been trained appropriately — e.g., if it’s not aware of a product defect or recall.

2. Undisclosed Sponsored Prioritization and Bias

When AI agents recommend products, suppliers or services (e.g., in the capacity of a personal shopper for a consumer), they may optimize paid relationships, affiliate revenue, or internal margin goals. If those incentives aren’t disclosed to the consumer, retailers face exposure under FTC endorsement and consumer protection rules. If a retailer has a disclosure and clearance policy for influencers, endorsements, affiliate programs, and similar relationships, now is the time to explore how the same principles can be applied to agentic systems’ training and judgment protocols.

In recent years, the FTC and state Attorneys General have begun monitoring and enforcing against the use of AI tools to enable deceptive or unfair trade practices, including generating fake endorsements or consumer reviews. Retailers may even face class-action litigation if the actions of an agentic AI tool misleads consumers or causes harm. Ultimately, the liability will fall on the retailer as the AI’s “supervisor.”

3. Dynamic Pricing and Discrimination Risk

Agentic AI frequently drives real‑time pricing, promotions and offers. However, when those systems lean on ZIP codes, device signals, inferred demographics, or behavioral proxies, they can unintentionally generate discriminatory outcomes or overstep privacy protections. State lawmakers are beginning to respond, with New York’s Algorithmic Pricing Disclosure Act being the first law to require clear disclosures whenever personal data is used to influence dynamic pricing.

4. Dark Patterns and Subscription Traps

AI agents calibrated to prioritize business goals and optimize engagement without risk thresholds replicate — or amplify — deceptive online design practices that trick or trap consumers into unwanted purchases or fees.  These can include tactics such as serving manipulative “limited time” or urgency offers, hiding additional fees until the last step of a purchase process, binding consumers to subscription programs without passing through the appropriate disclosures, or making it unreasonably difficult for a consumer to cancel their subscription. When these practices are automated and personalized, retailers may face heightened enforcement risks under state and federal automatic renewal and consumer protection statutes. The FTC is keenly focused on subscription programs and is poised to take action against companies that deploy AI systems to increase conversions at the expense of compliance.

5. Lack of Contractual Authority

Agentic AI’s ability to negotiate terms, accept conditions, or trigger purchases without human review raises a critical question: What authority does AI have to bind the retailer or the consumer? If an AI agent accepts unfavorable arbitration clauses, auto‑renewals, or pricing terms, retailers may find themselves locked into obligations they never intended to assume. Typical agency law will likely apply to these situations — i.e., the AI was acting on behalf of the retailer and cannot be held independently responsible.

6. Accountability and Explainability Gaps

When something goes wrong, regulators and plaintiffs will ask: What did the AI decide? Why did it decide that? And who is responsible? Agentic systems complicate each of those questions. Retailers that cannot explain decision logic, objective functions, or escalation pathways may struggle to defend AI‑driven outcomes, even if no human ever “approved” them. Robust recordkeeping practices and audit logs are crucial.

7. Privacy and Automated Decision‑Making Compliance

Agentic AI relies on continuous data ingestion, profiling, and inference. That creates risk under evolving privacy laws regulating automated decision‑making, sensitive data, and consumer profiling. With the EU AI Act and U.S. states like California poised to increase regulatory oversight of AI under the CPRA and similar statutes, retailers must map data flows, obtain appropriate consent, and ensure transparency, particularly where AI decisions materially affect pricing, eligibility, or access.

8. Insurance and E&O Coverage Gaps

Traditional insurance policies often assume human decision‑making. As AI agents act autonomously, retailers may discover that errors, omissions, or consumer harm caused by AI fall into coverage gray zones. Without proactive review, companies risk learning too late that AI‑driven conduct is excluded or insufficiently covered. Accordingly, retailers’ insurance and errors and omissions (E&O) policies may also need to be reevaluated to ensure they capture AI-driven operational risk.

9. Vendor and Platform Liability

Retailers frequently deploy agentic AI through third‑party platforms or integrations. However, outsourcing technology does not outsource liability. If an AI vendor’s system violates consumer protection or privacy laws, regulators will still look to the retailer that deployed it. Therefore, contractual protection, audit rights, and usage controls are essential.

10. Governance Drift and Lack of Human Oversight

Perhaps the most underestimated risk is organizational. Agentic AI requires a shift from reviewing outputs to governing actions. Without clear authority limits, escalation rules, red flag “kill thresholds” and cross‑functional ownership, retailers risk deploying systems that quietly expand their own scope, creating compliance exposure long before anyone realizes it. To mitigate this risk, retailers must also implement human-in-the-loop oversight mechanisms and employee training.

The Bottom Line

Regulators will treat AI decisions as company decisions, and the absence of human intent will not shield retailers from liability. As agentic AI becomes commonplace in retail operations, legal, marketing, product, and technology teams must align around guardrails that control what AI can do, not just what it can say.

Retailers that address these risks early can unlock the benefits of agentic AI while avoiding the regulatory and reputational fallout that comes from deploying autonomy without accountability.