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Understanding LLM Agents
From Reasoning and Tool Use to Memory, Planning, and Multi-Agent Systems
An accessible guide to how large language models become agents that can reason, use tools, remember, plan, interact with software, and collaborate with other agents. Written for readers who understand the basics of LLMs but do not want a mathematics-heavy treatment.
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An LLM can answer a question. An agent can keep working. Understanding LLM Agents explains what changes when a language model becomes part of a system that can reason, use tools, remember, plan, interact with software, and collaborate with other agents.
Written for readers who understand the basics of large language models but do not want a mathematics-heavy treatment, the book develops the core architecture step by step: the observe-decide-act loop; ReAct; retrieval, code, and tool calls; memory and reflection; planning; manager-worker and handoff patterns; agent-to-agent communication; MCP and A2A; digital environments; agent-computer interfaces; reliability; evaluation; and human oversight.
The book also places modern agents in the longer history of symbolic AI and reinforcement learning, and connects foundational research such as ReAct, Reflexion, Voyager, Generative Agents, SWE-agent, and tau-bench to the practical agent stack of 2026.
The result is a stand-alone guide to the ideas behind today's agentic AI systems, with minimal mathematics and an emphasis on architecture, examples, and engineering tradeoffs.
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