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Building Reliable AI Agents with PydanticAI: A Practical Guide to Structured Outputs, Agentic Workflows, and Production Python AI
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Publisher: Independently Published
Pages: 248
Weight: 0.96
Height: 0.52 Width: 7.00 Depth: 10.00
Language: English
Tired of AI demos that look impressive but fall apart when outputs change, tools fail, or real users depend on them?
Building Reliable AI Agents with PydanticAI shows you how to move beyond fragile prototypes and build AI applications that are structured, testable, secure, observable, and ready for real-world use.
You do not need previous experience with PydanticAI, AI-agent frameworks, MCP, orchestration systems, or production AI infrastructure. If you have practical familiarity with Python, this book guides you forward step by step, explaining not only what to build, but why each engineering decision matters.
You will start with your first typed PydanticAI agent and progressively develop a complete production-oriented system using structured outputs, dependency injection, tools, retrieval-augmented generation (RAG), multi-agent workflows, MCP integrations, testing, observability, security, durable execution, FastAPI, and containers.
Mistakes are treated as part of learning. Validation failures, broken tool calls, failed tests, and unexpected model responses become opportunities to understand the system and make it stronger-one practical improvement at a time.
Key FeaturesStep-by-step introduction to PydanticAI and production AI engineering
Hands-on development of typed, validated AI agents in Python
Practical coverage of RAG, MCP, tool calling, and multi-agent systems
Reliable agent workflows using Pydantic Graph
Testing and evaluation with deterministic tests and Pydantic Evals
Security patterns for authorization, prompt injection, permissions, and human approval
Observability with Logfire and OpenTelemetry
Durable workflows, failure recovery, FastAPI APIs, workers, and container deployment
Practical exercises and focused takeaways throughout
You will learn how to build typed agents, manage model providers and dependencies, design structured outputs, validate model responses, connect agents safely to APIs and databases, manage conversation and persistent state, implement RAG, orchestrate deterministic and multi-agent workflows, integrate MCP servers, test probabilistic AI behavior, trace failures, apply retry and fallback strategies, secure agent actions, and deploy long-running production AI systems.
Who This Book Is ForThis book is ideal for:
Python developers entering AI-agent development
Beginners to PydanticAI and agentic AI frameworks
Self-learners who want a practical path from prototype to production
Software engineers building dependable LLM applications
Developers interested in RAG, MCP, multi-agent systems, AI testing, security, and deployment
PydanticAI Foundations and Your First Typed Agent
Dependencies, Runtime Context, and Application Architecture
Structured Outputs and Reliable Validation
Tools and Controlled Agent Actions
Conversation Context, Persistent State, and Retrieval
Agentic Workflows with Pydantic Graph
Multi-Agent Systems and Coordinated AI Workflows
MCP and External Capability Integration
Testing and Evaluating Reliable AI Systems
Observability, Security, and Reliability Engineering
Durable Execution and Production Deployment
Build AI agents you can understand, test, secure, observe, and trust. Start your journey from your first typed agent to a production-ready AI system today.
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