Artificial intelligence (AI) has transitioned from a hypothetical concept to a tangible element in daily developer tasks and customer-facing products, with many teams actively incorporating it into their work. Code generation is the most affected area, highlighting potential reliability issues in AI-impacted code paths. A survey of over 150 developers and technical leaders from enterprise-scale companies in North America and Europe revealed that while nearly half are still in exploration or prototyping stages, 38% consider AI essential or are scaling it in production. However, confidence in observing and debugging AI workflows remains low, with only 13% feeling very confident, and 62% experiencing measurable losses in time or revenue due to reliability problems. As teams prioritize reliability, compliance, automation, and debt reduction over the next 12–24 months, they are adopting patterns like guarded tool calls, idempotent side-effects, human checkpoints, deterministic plans, and cost and policy budgets to improve AI system stability. Durable Execution is becoming crucial for managing state, retries, and long-running tasks, with a significant uptake in orchestration adoption, especially within large companies. The widespread use of OpenAI Agents SDK, Google's ADK, and LangChain reflects a trend towards integrating orchestration in AI workflows, emphasizing the importance of durable, observable workflows and treating reliability as an essential product requirement.