LLM Agents

LLM agents combine language models with tools, memory, and execution environments to pursue goals through sequences of decisions rather than producing a single response. Common approaches include ReAct-style planning and tool use, retrieval and reusable skill libraries, reflection and verification, multi-agent coordination, and training with supervised trajectories or reinforcement learning; runtime harnesses also manage state, permissions, and model routing. They are tested on interactive web and coding tasks, tool-use and long-horizon benchmarks, domain workflows, and simulations of social or physical settings. A central challenge is making agents reliable and safe when tasks, instructions, and environments change while controlling cost and evaluating performance beyond narrow benchmark success.