So, the hype around "AI agents" finally got to our CTO. We were tasked with finding a "low-code, high-impact" automation for our store managers. Lindy seemed to check the boxes. We rolled it out to about 200 users across our retail chain, thinking it would handle scheduling, basic inventory queries, and summarizing daily sales reports.
The promise was that it would learn and adapt. What actually happened was a masterclass in how brittle these systems can be outside a demo environment.
The first major break was with scheduling. Lindy would accept a manager's request to "book a meeting with the district manager next week," and it would dutifully check calendars and send invites. Sounds great. Except it couldn't parse our retail-specific context. "Next week" in a retail chain during the holiday season means Monday to Sunday, not Monday to Friday. We had district managers getting meeting invites for Sundays at 8 AM because a manager, thinking in terms of the retail week, said "next week." The lack of domain-aware temporal logic caused genuine operational friction.
Then came the inventory queries. Lindy was integrated with our inventory database to answer "how many units of SKU X do we have at store Y?" On the surface, it worked. But it had no concept of data quality or thresholds. A manager would ask, and Lindy would spit back a number pulled directly from the overnight batch sync. If that sync had failed or was partial—a common enough occurrence—Lindy presented the stale number as absolute fact, with no caveats. It took us three weeks of "but Lindy said we had it!" stockouts to trace the issue back to the agent's unjustified confidence.
Finally, the reporting summaries. It would take a dense sales report and generate a "key takeaway" paragraph. The sardonic part is that its summaries were consistently... fine. Bland, but not wrong. The breakage was more subtle. It would highlight a 2% week-over-week drop in a minor category but completely miss a 15% spike in shrink (theft) because that metric was buried in a table footnote. Its prioritization was based on textual prominence in the document, not business impact. So managers were focusing on the wrong things.
The takeaway? We're treating it like a very eager, very literal-minded intern now. It requires intense supervision. The idea that it can "autonomously" handle these workflows was, in our case, wildly optimistic. The failures weren't in the AI being "stupid," but in its inability to handle the edge cases, context, and data quality issues that define real-world retail operations.
Data skeptic, not a data cynic.