Etsy is making it easier for buyers to find and purchase items from domestic sellers as a way to blunt potential tariff-related price increases on imports. These include features for customers such as curated shopping pages and local seller spotlights. For sellers, Etsy is providing an online tariff handbook that provides information such as how tariffs are collected and the definition of the de minimis exception. According to Etsy data, 89% of its sellers work from home by themselves without complex overseas production lines and fulfillment requirements, and most source supplies domestically, which can help them remain “agile and resilient” even when there are shifts to global supply chains. Etsy said it is also “elevating seller voices” to advocate for public policies that reflect the “unique needs” of small businesses and protect their ability engage in global commerce. Etsy has been attempting to improve its search experience for customers and sellers. The e-tailer recently began letting customers browse by curated collections on its app, based on trends, aesthetics and occasions. The retailer, which also provides set of creativity standards for products sold on its site, optimizes its search results to showcase a broader range of items from more sellers. Etsy has also launched a search visibility page that provides specific actionable tips, including insights on listing image quality and quantity, return policies, message response times, and shipping prices, for certain seller listings.
Monte Carlo’s AI agents for observability investigate, verify, and explain the root cause of specific data quality issues while also providing the recommended next steps for resolving
Monte Carlo, the data + AI observability platform, announced the launch of observability agents, a suite of AI agents built to accelerate monitoring and troubleshooting workflows to improve data + AI reliability. Monte Carlo’s Monitoring Agent recommends data quality monitoring rules and thresholds, which can then be deployed with the push of a button. The Troubleshooting Agent investigates, verifies, and explains the root cause of specific data quality issues while also providing the recommended next steps for resolving them. Both agents are the first of their kind in that they are not just making simplistic recommendations based on data profiles, but leveraging a sophisticated network of LLMs, native integrations and subagents to gain full visibility into the data estate across data, systems, transformation code, and model outputs. Monte Carlo’s Monitoring Agent, now generally available, identifies sophisticated patterns and relationships across a dataset that would otherwise be missed by more traditional profiling methods. Monte Carlo’s Troubleshooting Agent, with a general release scheduled for Q2 2025, investigates, verifies, and explains the root cause of specific data + AI quality issues. The agent tests hundreds of different hypotheses across all relevant tables within a dataset to understand if the root cause of a specific issue is a result of receiving bad data from the source, an ETL system failure, a transformation code mistake, or incorrect model output. The observability agents automate powerful monitoring and resolution tasks, but never directly manipulate, change, or act upon your critical data and key systems (read-only). This ensures they don’t create more reliability issues than they help resolve.