Shaping Agent Behavior with Gherkin, Envelopes, and Schemas
The article discusses techniques for building reliable AI agents by describing desired behaviors instead of writing rules. It covers Gherkin-style prompts, message envelopes, and structured completion signals.
Why it matters
These techniques can help AI developers build more reliable and predictable agent behaviors, which is crucial for real-world applications.
Key Points
- 1Rules-based prompting leads to long, complex instructions that agents often ignore in favor of fluent-sounding output
- 2The behavioral science approach focuses on describing the context, trigger, and expected behavior for agents
- 3Gherkin scenarios define preconditions, triggers, and expected outcomes in plain language
- 4Message envelopes and structured completion signals provide a clear operating contract for agents
Details
The article explains how the authors shifted from a programming mindset to a behavioral science approach when building complex AI prompts. Instead of specifying rules that agents often ignore, they describe the desired behaviors using Gherkin-style scenarios. This approach maps more naturally to how large language models were trained. The article also covers the use of message envelopes and structured completion signals to give agents a clear operating contract. Together, these techniques aim to make AI agents more reliable in following the intended behaviors, even in edge cases and failure modes.
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