How to Build From AI Agent Examples

Developing AI agents for reliable production workflows involves moving beyond simple demos and understanding essential components like decision loops, tools, states, evaluations, and traces. The process begins with identifying a concrete business workflow and then determining the appropriate agent pattern, such as tool-using, planner-executor, reviewer, router, or multi-agent workflow, each suited for specific tasks. Key steps include defining a clear goal, specifying context and tools, setting state and exit criteria, and narrowing the scope of the first version for testing. Prompts should be treated as versioned application artifacts, and tools must be categorized by permissions and reversibility. Effective orchestration, tracing, and evaluation from real examples are vital, ensuring that agents can handle failures and not just ideal scenarios. The development process involves staged releases with clear promotion metrics and an iterative loop for continuous improvement. This method turns AI agent examples into controlled workflows with reliable evaluations, versioned prompts, and traceable outputs, ultimately creating a robust system.