How AI Agents Work: An Architectural Deep Dive
The article provides an in-depth analysis of AI agents, focusing on their architecture and operational patterns. It explains the ReAct pattern, which is fundamental to the functioning of large language models. Additionally, it discusses the importance of surrounding infrastructure and tool design in enhancing the performance of AI agents.
- ▪AI agents are defined as large language models connected to external tools that operate in a reasoning loop.
- ▪The ReAct pattern is the foundational architecture for production AI agents, allowing them to effectively complete tasks.
- ▪The performance of AI agents relies heavily on the management of context windows and the design of tools.
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PostHow AI Agents Actually Work: An Architectural Deep DiveAn analysis of the patterns, infrastructure, and trade-offs behind the systems that have redefined what large language models can doPublished May 21, 2026 Reading time 89 min read Table of Contents ▾Executive Summary1. Definitions: What Is an “Agent” and How Does It Differ from Other AI Systems?2. The ReAct Pattern: Core ArchitectureHow the Loop WorksWhy It WorksA Minimal ReAct ImplementationPerformanceMechanistic Analysis: Why Interleaving Works (and When It Does Not)3. How Models Learn to Be Agents: Training MethodologySupervised Fine-Tuning on Tool-Use TrajectoriesPreference Optimization: Teaching Models When to Use ToolsReinforcement Learning from Environment FeedbackPrompted vs. Fine-Tuned: The Trade-off4.
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