Structured Workflows Cut AI Agent Hallucinations, Lifting Task Success from 60% to 94%

A development team rebuilt a multi-step AI research agent after its original 12-prompt chain failed roughly 40% of the time due to malformed outputs, missed hallucinations, and cascading errors between steps. The redesigned system replaced implicit prompt chaining with a structured workflow using Pydantic schemas to enforce typed inputs and outputs at every stage. Automated guardrails now validate each step's result and trigger retries before passing data downstream, while an evaluation harness scores performance per step rather than only at the final output. The changes brought task success rate from a 60% baseline up to 94%, while also making failures easier to isolate and debug. The team published their approach in January 2024, sharing code patterns for schema definitions, guardrail logic, and per-step evaluation.