Beyond Workflows: The Case for Adaptive Intelligence in .NET Agents

.NET developers are rapidly adopting workflow-driven orchestration as the dominant architectural pattern for agentic AI, but this approach fundamentally limits how systems handle unpredictable tasks and unexpected failures. While workflow-driven systems excel at structured processes with predefined steps, they fail to leverage the LLM's natural ability to reason about novel failures and adapt to unexpected situations. The ecosystem needs genuine architectural alternatives that enable LLM-driven reasoning for failure recovery and decision-making, rather than relying solely on pre-written error handling code.