Google: Introduction to Agents (www.kaggle.com)

Google published a whitepaper introducing "Agents" as a new AI paradigm that moves beyond single-turn prediction or generation toward autonomous problem-solving systems. Unlike traditional models that require step-by-step human prompts, an agent is framed as a full application: it plans, decides, and takes actions to achieve user goals. The paper—authored by Alan Blount, Antonio Gulli, Shubham Saboo, Michael Zimmermann, and Vladimir Vuskovic—positions agents as the bridge between a language model’s reasoning power and the practical ability to act in multi-step, real-world workflows. For the AI/ML community this matters because it reframes research and engineering priorities from isolated model capabilities to integrated agent architectures that handle planning, tool use, memory, and long-running tasks autonomously. Technically, agents combine LM-based reasoning with action primitives and orchestration logic so they can infer next steps without continual human direction, enabling complex end-to-end automation. That shift has broad implications: new design patterns and benchmarks for planning, robustness and safety controls, infrastructure for persistent state and tool integration, and evaluation metrics focused on goal completion rather than single-turn correctness. In short, Agents represent a move from building smarter predictors to building systems that can operate independently to solve real-world problems.