Six months into running a Lindy agent on my wedding venue inbox, the agent was doing exactly what I asked it to do. Replies were on-brand, response times dropped, my inbox stopped being a four-hour-a-day job. The numbers said it was working.
Then I noticed I was asking the wrong question. Lindy was a great responder. What I actually needed was an agent that could think. One that surfaced the intelligence buried in eighteen months of inquiries: which couples were stressed about budget across three threads, which inquiry sources predicted the highest-margin events, which leads were leaking out of the funnel at the same point every time. Lindy was not built for that, and the no-code abstraction made the missing capability invisible until the data was sitting right in front of me.
So I rebuilt the agent in custom code. This talk is the migration story. I'll show the Lindy setup live, walk through the production data that made me migrate, and demo the custom architecture that now runs in production. Comparative cost numbers, comparative reply quality, and the specific intelligence patterns Lindy structurally could not surface, with the inquiries that proved it.
For developers building agents, the talk is a working framework for deciding when no-code platforms are the right answer (mine: month one through six) and when custom becomes the right answer (mine: month seven onward). Same job on paper, different jobs in production. Happy to teach Lindy Flow building if you would rather have a hands on element.

Key Takeaways (3-5 bullets)

A working framework for deciding when no-code agent platforms are the right tool, and when custom code becomes the right tool. With production cost and quality data from both sides of the same migration.
The architectural distinction between agents that respond and agents that surface intelligence — and why most teams don't realize they need the second until the first is already running.
Three specific inquiry patterns a no-code agent structurally cannot detect, and the prompt and orchestration patterns my custom agent uses to catch them.
Real production metrics from eighteen months of agent deployment, including the six months on Lindy and the twelve months on custom code.
The migration playbook: when to start with no-code, what signals tell you it's time to migrate, and the specific intelligence-surfacing capabilities that justify the cost of custom.