How We Use Gherkin, Envelopes, and Schemas to Shape Agent Behavior

Instead of writing rules agents ignore, we describe the behaviors we want. A look at how Gherkin scenarios, message envelopes, and structured output formats work together to make AI agents reliably do the right thing. When we first started building more complex prompts, we took the programming mindset to prompting: we wrote instructions, added more rules when something broke, and ended up with prompts that read like policy documents. After several iterations of watching agents ignore nuanced rules in favor of fluent-sounding output, we started looking for a different approach. We shifted to a behavioral science mindset. Instead of specifying what the agent must do, we describe the context it operates in, the outcome we want, and concrete examples of what success and failure look like. It is harder to design — each scenario requires thinking through not just the happy path but the edge cases and failure modes. But in production, it is much more reliable in our experience. This post cove