Structured Outputs Are the Contract Your AI Agent Is Missing
This article discusses the problem of free-text agent responses in AI-powered workflows and how structured outputs can solve this issue by defining a clear contract between the language model and the downstream automation.
Why it matters
Structured outputs are a key pattern for building reliable AI-powered automation in regulated industries and workflows.
Key Points
- 1Free-text agent responses are difficult to process and automate downstream
- 2Structured outputs, defined as a JSON object with a schema, provide a clear contract for the language model to follow
- 3Structured outputs enable trivial downstream logic, avoiding fragile NLP problems
- 4The schema must be clearly communicated to the language model in the prompt
Details
The article explains that in regulated workflows like benefits verification or clinical trial site selection, the AI agent's output needs to drive downstream automation, such as updating records, creating tasks, and triggering workflow steps. However, relying on free-text summaries from language models leads to inconsistencies and fragility, as the models do not guarantee consistent phrasing. The solution is to define a structured output schema, where the language model is asked to extract specific fields and return them as a JSON object. This structured output provides a clear contract between the language model and the rest of the system, enabling simple conditional logic for downstream processing. The article emphasizes the importance of clearly communicating the expected schema to the language model in the prompt.
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