Building AI agents that perform reliably in production requires a shift from demo setups to robust engineering practices. While AI is integrated into workflows, its real-world impact is often limited by a "context problem," where human oversight remains crucial.

Leading teams are adopting principles similar to the Twelve-Factor App, focusing on control flow, state management, and scope. Key practices include owning prompts and the context window, ensuring relevance over volume, and precise tool definitions. The core idea is "context engineering": deliberately controlling what the model sees on every call.

Production agents function as a loop where the model receives context and outputs a decision. However, common failure patterns emerge under real traffic: compounding errors in chained calls, confidently wrong output (hallucinations), excessively long loops, and loss of work state. Addressing these requires deterministic code around the model, limiting its control to specific decision points.