In Depth

Modern LLM-based agents use a plan-act-observe loop: the model generates a plan, calls tools to execute steps, observes results, and iterates until the goal is met. Frameworks like LangChain, AutoGPT, and the Claude Agent SDK provide scaffolding for building agents. Key challenges include task decomposition reliability, error recovery, cost management, and safe handling of irreversible real-world actions.