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AI systems fail in ways you’ve never seen before. A model scores high on benchmarks and still misleads users. An agent completes a task by taking harmful actions and then covering its tracks. A RAG pipeline retrieves the right documents and still returns a fabricated response. Evaluating AI Systems teaches you how to know—and prove—whether your LLM-based applications are ready to ship. In this practical and important book, author Panos Alexopoulos provides a data and decision-driven approach that starts with the questions you need to answer about your system and 

and works backwards to the evidence you need to gather.

Changing an LLM to alter an agent’s behavior is costly and slow. In Self-Improving Agents: How to engineer adaptive agent harnesses, Micheal Lanham, author of AI Agents in Action, shows you how to design AI agents that measurably get better in production without fine-tuning, weight updates, or waiting for the next model release.

You’ll do this by building HelixAgent—a RAG general-knowledge agent that gains drift detection, rollout controls, and a reward-hacking runbook—along with a portfolio including a data-analyst agent, a helpdesk agent with four-tier memory, a hill-climbing research agent, and self-

In ordinary software, a crash, security breach, or data loss can be a big problem for your business. In the software running flight controls, railway signals, defibrillators, autonomous vehicles, and other safety-critical systems, it’s literally a matter of life and death. Memory Safe C++ shows you how to write verifiable, deterministic-by-design systems when failure is not an option. In it, you’ll learn the ins and outs of memory safety in C++ as you master practical principles of fail-safe operations, worst-case execution, and how to spot risky code—even when it compiles clean and passes every test.