Overview

In the twelve months leading up to 2025, we crossed several invisible thresholds. Open-source models surpassed proprietary frontiers in code generation quality. Frameworks like LangChain, LlamaIndex, AutoGPT derivatives, and emerging agent-native platforms reduced the overhead of building multi-step agents from months to hours. Tool-calling became standardized across major providers, allowing agents to reliably interact with external systems-file systems, databases, web APIs, Git repositories, cloud consoles, and even other agents. Long-context windows grew to hundreds of thousands of tokens, making it practical for an agent to hold an entire medium-sized codebase in working memory. Simultaneously, the cost of inference fell below levels where experimentation became essentially free for individuals and startups. A capable coding agent that would have cost thousands of dollars per month in 2023 now runs comfortably on a laptop with local models or costs pennies per task in the cloud. These technical milestones were matched by cultural ones. Tens of thousands of developers began shipping production code written primarily by agents. Companies quietly rewrote internal tools, migrated legacy systems, built customer-facing features, and automated entire departments using agentic workflows. The early results are staggering: 10× to 50× reductions in development time for certain classes of problems, dramatic drops in bug rates when agents are paired with rigorous test suites, and the sudden viability of hyper-personalized software that would have been impossible to maintain manually. Yet most educational resources still treat AI as a glorified autocomplete. They teach prompt engineering tricks, one-shot code generation, or simple chat interfaces. That is already obsolete. The frontier has moved to systems that act persistently, maintain state across hours or days, coordinate with multiple specialized sub-agents, and take responsibility for outcomes rather than merely suggesting snippets. This book closes that gap.

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