I've been running an interesting experiment the last 14 days. I've focused heavily on churn and starting working on re-implementing customers that got stuck on AI agent, getting hands on with customers. I started with a customer who'd spent months in onboarding, built 14 custom actions, but never went live. The reason? A trust standoff: they wouldn't turn the AI on until it was proven. So I started working on solutions for them with the help of our team 1. Testing before going live. We built a test suite of 31 scenarios and ran simulations through Cortex (our internal AI agent). Each time a simulation would fail, Cortex would trigger trigger our merchant facing agent Gaia to fix them. Running this loop of autopilot compressed days of work in a few hours. 2. Tone of voice. The customer had an 10+ pages tone of voice policy. Instead of manually tweaking it over weeks, I used a similar approach, simulate hundreds of conversation to find the exact right wording of the tone of voice prompt. 3. The long tail of requests. Custom widgets, integration fixes, edge cases, you name it. This stuff that eats lots implementation time. So I put Cortex directly in the shared Slack channel with the merchant. When they asked for order hold/cancel/resubmit buttons, Cortex read the API docs, confirmed the endpoints, and built the configs. Taking these learnings home: - We're prioritizing trust building. You can now run simulations via the MCP (you might need to reload the latest tools), and it's coming to the product in Sept. - We're accelerating the pace of iteration, putting Cortex on more re-optimization and implementations of customer. The goal is that each implementation makes the next one easier by compounding learnings. 5 of 6 merchants are now live with this setup. We're treating implementation and re-optimization as a product, with a PM who builds, tests, and ships. When you're behind on a metric, the fastest way to solve it for me is usually to get my hands dirty, using our own product, and accelerating customer feedback loops. Excited to now scale this approach further!