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Written by Max Zeshut
Founder at Agentmelt
The process of extracting structured data from an AI model's free-text output—converting natural language into JSON, database records, API calls, or other machine-readable formats. Reliable output parsing is essential for AI agents because downstream tools and integrations require structured inputs. Techniques include JSON mode (constraining model output to valid JSON), function calling (the model emits structured function invocations), regex extraction, and grammar-constrained decoding. Parsing failures are a common source of agent errors in production.
An agent analyzes an invoice and needs to extract vendor name, amount, date, and line items into a structured JSON object. Output parsing ensures the model's response is valid JSON with the expected schema—not a conversational summary with the data buried in prose.