AI agents and agentic workflows represent a fundamental shift in how autonomous systems are architected. Traditional imperative programming requires humans to specify exact steps, whereas agents instead define objectives and delegate decision-making to language models equipped with tool-use capabilities. This distinction matters because real-world tasks involve uncertainty, environmental interaction, long-horizon reasoning, and dynamic replanning — challenges that make it impossible to hardcode every edge case, anticipated failure mode, and contingency path at scale.
Agentic workflows address this complexity through closed-loop reasoning, in which agents observe their environment, plan actions, execute them, receive feedback, and adapt accordingly. This paradigm underpins production systems at Anthropic, OpenAI, Google DeepMind, and major enterprise AI platforms. At this stage of the curriculum, the focus turns to advanced lifecycle management, state supervision, tool orchestration at scale, and robust error recovery patterns — the capabilities that distinguish production-grade agents from prototype demonstrations.