Building Applications with AI Agents

A practical guide to building AI agents

Building Applications with AI Agents focuses on the hard part of the agent conversation: turning promising model demos into systems that can actually be designed, evaluated, and deployed. Michael Albada takes a systems-first view of agentic software, with clear attention to the trade-offs that matter in production—speed, accuracy, reliability, scalability, and cost.

The book is especially useful for readers who want more than a conceptual overview. It moves through the core parts of agent development: choosing models, integrating tools, managing memory, designing orchestration, and deciding when a single agent is enough versus when a multiagent structure makes more sense. Along the way, it connects the theory of foundation-model-based agents with practical implementation concerns.

What the book covers

• Defining AI agents and the broader shift toward agentic systems
• Core components such as tools, memory, orchestration, and context engineering
• Single-agent and multiagent architectures, including collaboration and coordination patterns
• User experience design for synchronous and asynchronous agent interactions
• Common frameworks and ecosystems, including LangGraph, AutoGen, CrewAI, and the OpenAI Agents SDK
• Memory approaches such as retrieval, vector stores, GraphRAG, and knowledge graphs
• Learning approaches ranging from experiential methods to fine-tuning and preference optimization
• Validation, measurement, and testing strategies for real-world agentic systems

Built for builders, not just observers

This is a strong fit for developers, engineers, technical product teams, and AI practitioners who are actively thinking about how to structure an agent application rather than simply experiment with prompts. The emphasis on architecture, design choices, and measurable behavior makes it especially relevant for teams that need dependable systems, not just impressive prototypes.

If you are comparing frameworks, planning a production workflow, or trying to understand how agent systems should be composed and evaluated, this book gives you a solid map of the territory.

Why it stands out

Many AI books stop at examples. This one pays close attention to system design. That makes it valuable for readers who need to reason about agent behavior across multiple steps, multiple tools, and multiple model calls. It is the kind of title that rewards careful reading and practical application.