SKU/Artículo: AMZ-B0GP6H6TJK
Agentic Backend Engineering with Python: Design, Secure, Test, and Deploy AI Agents with FastAPI, Tools, Memory, Guardrails, and Observability
Format:
Paperback
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.74 kg
0.74 kg
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Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Modern Agentic Backend Engineering with Python is a definitive, production-focused guide to designing, securing, testing, and deploying AI-powered backend systems that move beyond chatbots into reliable, scalable, and auditable agentic architectures. Grounded in real backend engineering principles and built around FastAPI, structured outputs, tool calling, memory, guardrails, and observability, this book shows how to transform large language models into disciplined planning components inside robust backend services.Agentic systems represent the next evolution of backend architecture where AI models don’t just generate text, but participate in decision-making, planning, and multi-step workflows. This book treats AI as an untrusted planner and your backend as the execution authority, ensuring safety, determinism, and reliability. You’ll learn how to build agent-driven APIs that meet the same production standards as any mission-critical service: secure, observable, cost-aware, and testable.Written for professional engineers, this book aligns with modern backend best practices, distributed systems design, and API-driven architectures. It reflects real-world production concerns security boundaries, schema validation, policy enforcement, idempotency, retries, and monitoring bridging the gap between experimental AI demos and deployable systems.You’ll design and implement a complete agentic backend using Python and FastAPI, progressively building a production-grade Support Triage system. By the end, you’ll know how to orchestrate agents as bounded workflows, connect them safely to tools and data, enforce contracts with structured outputs, add memory and retrieval, apply guardrails, test behavior, and ship with confidence.What’s Inside:• Designing agent endpoints that aren’t chatbots • Structured outputs as enforceable API contracts • Tool and capability modeling with strict schemas • Memory vs. state, and safe context management • Retrieval-Augmented Generation (RAG) with evidence grounding • Guardrails against prompt injection and tool misuse • Automated testing, evals, and regression strategies • Reliability patterns: retries, fallbacks, circuit breakers • Observability: tracing, debugging, and quality monitoring • Deployment, performance tuning, and cost controlBuilt for backend developers, Python engineers, software architects, and AI practitioners who want practical, production-ready guidance not theory or hype. If you care about reliability, security, and maintainability as much as innovation, this book is for you.Gain usable, real-world skills in weeks, not months. Each chapter builds directly on the last with concrete patterns and working examples, so you move from concept to production architecture quickly.If you’re ready to move past toy LLM integrations and start building serious agentic backends, this book is your blueprint. Get your copy today and begin engineering AI systems that are safe, predictable, and ready for production.
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AR$56.297
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AR$140.738
60% OFF
AR$56.297
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