As AI agents become more capable, many engineering teams embed increasingly complex business logic directly into system prompts. While effective in prototypes, this approach creates invisible technical debt: nondeterministic failures, brittle model upgrades, and regression cycles that are difficult to reason about or validate.In this technical case study, Stanko Kuveljić present the refactoring of a production scheduling agent from prompt-encoded logic to a deterministic, state-driven architecture. By separating model reasoning from application state transitions, the team introduced explicit invariant enforcement, structured observability, and behavioral testing gates across the continuous integration and continuous delivery (CI/CD) pipeline.Key topics include:Designing state graphs that enforce deterministic routing for business-critical operationsImplementing behavioral validation pipelines that test agent tool calls and state mutations instead of conversational phrasingReducing prompt surface area to improve traceability, reproducibility, and model upgrade safetyIntroducing deployment gates that reduced manual regression validation effort in productionAttendees will leave with a reference architecture, a behavioral testing checklist, and a step-by-step approach for migrating prompt-driven agent systems toward bounded, production-grade reliability.