1 What is an AI agent?

Recent breakthroughs in large language models (LLMs) and retrieval-augmented generation have catalyzed a surge of interest in AI agents—systems that use an LLM as a reasoning engine to autonomously decide and act toward a goal. Unlike traditional software or a single LLM call, agents pair language understanding with tool use and dynamic decision-making, enabling them to research, compute, and interact with external systems. This power comes with trade-offs: multi-step reasoning raises cost and latency, and early mistakes can cascade, so choosing when to use an agent versus simpler logic or a single LLM call is a practical, business-critical decision.

An LLM agent operates in an iterative loop of reasoning, action, and observation, continually deciding what to do next, invoking tools, incorporating results, and determining when to stop. Agency exists on a spectrum—from simple LLM calls and fixed chains to routers, tool use, multi-step control, and even tool creation—each level granting more autonomy. The chapter outlines when LLMs are warranted (unstructured data, diverse inputs) and when full agents are justified (uncertain step counts, high task value, manageable error risk and detectability), illustrating with research and multi-step problem-solving scenarios that mirror real-world workflows.

The book takes a from-scratch path: begin with a basic ReAct agent that selects and uses tools; then layer in knowledge access via RAG for long-term memory, planning to map tasks into subtasks, and reflection for self-correction and continual improvement. It scales to multi-agent systems that divide complex work among specialized roles, and closes with monitoring and evaluation practices essential for debugging, performance, and reliability. Readers are guided through hands-on Python implementations, using common APIs and cost-aware techniques, to build intuition and confidence that transfer across frameworks and real-world deployments.

Example of a language model’s generalization capability.

User requests flow through the research agent, which branches into multiple searches and synthesis.

The LLM Agent's decision loop is an iterative process of LLM decision-making and tool use.

Progression of agency levels in LLM applications.

Progressive enhancement of agent architecture from basic LLM-tool integration (chapters 2-4) through advanced capabilities like memory, planning, and reflection (chapters 5-8) to multi-agent coordination (chapter 9).

Basic agent operational cycle demonstrating autonomous reasoning and tool use.

Agent with cognitive capabilities

Multi-agent roles for a software development task.

• An LLM agent is a program that determines the next action and evaluates goal completion (i.e., termination conditions) based on the output of an LLM, in accordance with a given objective and context.
• LLMs excel at generalization—handling diverse tasks without requiring task-specific retraining through capabilities such as few-shot learning, zero-shot learning, and reasoning with thinking tokens.
• Agency levels range from simple code execution to fully autonomous systems, with increasing autonomy as the LLM gains more control over task execution, action selection, and option determination.
• You should use an LLM agent for tasks that involve unstructured data analysis, require multiple unpredictable steps, have sufficient value to justify computational costs, and allow for error detection and correction.
• Building agents from scratch provides a deep understanding of core principles, clear visibility into the context flow, improved debugging capabilities, and the flexibility to create custom solutions that go beyond framework limitations.

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