The Microsoft team behind the Azure SRE Agent started with an overwhelming number of tools—over 100—and soon realized that this complexity was counterproductive. They faced issues with reliability and generalization, ultimately building a rigid workflow instead of a functional AI agent. The breakthrough came when they focused on "context engineering," which emphasizes the importance of how and when information is added for making decisions. They learned that every decision involves tradeoffs, like balancing latency with user oversight. By simplifying their approach, they replaced numerous narrow tools with just a couple of wide ones, specifically Azure CLI (`az`) and Kubernetes CLI (`kubectl`). This shift allowed for better context management and reasoning since the model already had familiarity with these command-line interfaces. The result was significant: less complexity, more capability, and improved reliability. The team then attempted to implement a multi-agent system, which initially appeared promising but ultimately revealed challenges related to coordination. The system struggled with handoffs and communication between agents, leading to inefficiencies and confusion. To address these issues, the team consolidated their approach, reducing the number of specialized agents and relying more on generalist agents equipped with broad tools. This restructuring allowed for more fluid interactions and effective problem-solving without the pitfalls of rigid boundaries. A practical example of their success came when the SRE agent autonomously diagnosed and resolved a deployment failure by navigating Azure’s resources, demonstrating the power of this new architecture. The focus then shifted to refining context management for longer conversations, moving towards more effective data handling strategies.