A customer buys a baby shower gift for a colleague just once, yet baby products continue to appear on the homepage every time they visit the website. A salesperson requests an expedited order for a special project, but the AI Agent then treats urgency as a standing requirement. Both situations stem from the same problem: the system sees a single action but cannot distinguish between a user’s long-term preference and a one-time exception.
If a company wants its AI Agent to understand users better over time, it must first decide how each memory should be stored, who can view it, and how incorrect information can be corrected. Simply collecting more data will also allow errors to accumulate. Once the AI Agent applies these memories to quoting, procurement, or customer service processes, a single misjudgment can affect subsequent recommendations.
Why shouldn’t a single purchase be treated as a long-term preference?
TechOrange reported that when Etsy relied only on a customer’s most recent purchase, buying a belt buckle could prompt the system to recommend belts or hats. Someone who purchased a baby shower gift might be misidentified as an expectant parent and continue receiving recommendations for baby products. Etsy therefore began using purchase history, personal preferences, and behavioral signals to build customer profiles, aiming to determine whether someone was preparing for school, moving house, collecting books, or buying a gift for another person.
The same problem can arise with enterprise AI Agents. A salesperson may agree to adjust a customer’s payment terms for an individual case, but that does not mean the customer will always prefer those terms. A procurement team may request a quote from another supplier because of a temporary material shortage, but that does not mean the original supplier has been excluded. A customer service team may escalate a one-off complaint, but that action should not automatically become the standing rule for every similar case. If the system remembers only the outcome without also recording the customer involved, the applicable period, and the reason, it may apply that information incorrectly the next time a similar case arises.
When designing memory fields, project teams should therefore separate the “preference” from the “conditions under which it applies” and retain the data source, date, and intended use. To review existing data, teams can begin by sampling recent transactions and conversations to determine whether each piece of information reflects the user’s own preference, an action taken on someone else’s behalf, or a response to exceptional circumstances. If the team cannot explain the distinction clearly, the information should not be treated as a long-term preference.
Classify information into four categories before writing it to memory
Whether a statement should be remembered depends on its intended use and the risks it may create. If a user explicitly says, “Please always provide quotations in Traditional Chinese,” that can be recorded directly as a preference. If a salesperson mentions that a customer may be reducing its budget, but the information has not yet been confirmed, it should first be reviewed by the person responsible for the account. If the information concerns credit terms or could change the customer’s classification, approval from a designated manager should be required. National identification numbers, health data, and private information unrelated to work should be explicitly prohibited from being written to memory, preventing the AI Agent from using it later for unrelated tasks.
Sales, compliance, and data governance teams should jointly establish four categories: may be remembered directly, requires review, requires approval, and must not be written. These guidelines should include real examples from each department rather than only abstract category names. For example, “The customer prefers electronic invoices” may be remembered directly, while “The customer may end the business relationship” requires review. If information cannot be classified, do not write it to memory until the appropriate owner has assessed it.
EgentWrX allows users to view, edit, and delete AI Agent memories. Administrators can also configure rules for direct writes, required reviews, and required approvals, while memories containing personal data are isolated. Companies adopting these mechanisms must still define responsibilities clearly, including who conducts reviews, how long unconfirmed information remains valid, and who handles user correction requests. A practical first step is to begin with low-risk scenarios, such as customer service response preferences and sales document formats. After confirming that the classification rules are clear, the company can consider adding information that may affect transaction terms.
Before expanding usage, confirm who has access and whether actions can be traced
The more departments a customer profile covers, the greater the potential impact if its data is misused. Information that customer service needs to process a return does not necessarily need to be available to marketing. Supplier negotiation terms held by procurement should not automatically appear in AI Agent responses generated for other departments. If a company opens access to all data first and adds role restrictions later, employees with no relevant business need may see sensitive information during the pilot.
In EgentWrX, companies can define, by organizational structure and role, which tasks each person may perform and which data they may access. Administrators can also use audit logs to review the work performed by an AI Agent and verify whether the records are complete. However, the existence of records does not prove that the AI Agent has delivered business results. Teams must evaluate recommendation quality separately from business metrics such as revenue and order value.
The original report identified personalized shopping experiences as one factor in Etsy’s recent performance improvement, but it did not quantify the contribution of AI customer profiles alone. During a pilot, companies likewise cannot attribute all revenue growth to the AI Agent simply because revenue increased over the same period. A better approach is to begin with a role-based access matrix that lists data types, authorized departments, permitted roles, and approvers. The company should then sample a set of records each month and verify the data source, memory-writing rule, output, human approval, and subsequent corrections before deciding whether to expand the system to other departments.
FAQ
How many times must an AI Agent observe a behavior before treating it as a long-term preference?
No fixed number applies to every situation. Companies should consider whether the user explicitly expressed the preference, whether the same behavior occurred repeatedly at different times, and whether the preference could affect pricing or the user’s rights. The higher the risk, the more important it is to obtain confirmation from the user or a manager before writing the information to memory.
Can users correct information that an AI Agent has remembered incorrectly?
Yes. Companies should allow users to view, edit, and delete the memories an AI Agent stores about them. If the information affects customer classification, credit decisions, or quotations, the company should also retain a record of the correction and confirm that subsequent processes are using the updated information.
What information should not be stored in an AI Agent’s long-term memory?
Private information unrelated to work, personal data without a clearly defined purpose, and unverified speculation that could harm another person’s rights should either be prohibited from memory or require approval before being written. Compliance, data governance, and business teams can jointly maintain this list and conduct regular sample reviews.
How can a company determine whether personalized recommendations are delivering business results?
Companies should track the AI Agent’s activity records separately from business metrics, then compare recommendation outcomes, human corrections, and customer responses before and after adopting its recommendations. Revenue and order value are also affected by pricing, marketing campaigns, and market conditions. An increase in the overall figures alone does not prove that the AI Agent produced all of the growth.
References
- TechOrange: Etsy AI Customer Profiles and Personalized Recommendations — TechOrange’s overview of Etsy’s AI customer profiles, recommendation scenarios, and shopping channel strategy.
