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 presents 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 operations
• Implementing behavioral validation pipelines that test agent tool calls and state mutations instead of conversational phrasing
• Reducing prompt surface area to improve traceability, reproducibility, and model upgrade safety
• Introducing deployment gates that reduced manual regression validation effort in production

Attendees 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.