The first step in building an AI agent is defining a clear use case, scope, and success criteria before choosing models or frameworks. Learn how to start the right way. When people decide to build an AI agent, they often begin in the wrong place. They compare frameworks, watch demos of multi-agent systems , or debate which model to use. That feels like progress, but most official guidance points somewhere else: the real first step is defining the exact job the agent should do, its boundaries, and how success will be measured. OpenAI , Anthropic , Microsoft, and Google Cloud all emphasize that strong agent systems begin with a narrow, well-defined use case rather than architecture-first thinking. That idea matters because not every AI application should be an agent in the first place. Anthropic distinguishes between workflows, where steps are fixed in advance, and agents, where the model decides how to use tools and complete tasks dynamically. Google Cloud similarly frames agent ...
The first step in building an AI agent is defining a clear use case, scope, and success criteria before choosing models or frameworks. Learn how to start the right way. When people decide to build an AI agent, they often begin in the wrong place. They compare frameworks, watch demos of multi-agent systems , or debate which model to use. That feels like progress, but most official guidance points somewhere else: the real first step is defining the exact job the agent should do, its boundaries, and how success will be measured. OpenAI , Anthropic , Microsoft, and Google Cloud all emphasize that strong agent systems begin with a narrow, well-defined use case rather than architecture-first thinking. That idea matters because not every AI application should be an agent in the first place. Anthropic distinguishes between workflows, where steps are fixed in advance, and agents, where the model decides how to use tools and complete tasks dynamically. Google Cloud similarly frames agent ...