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Building Reliable AI Agents: Build Reliable AI Agents with MCP, RAG, Memory, Tool Use, Multi-Agent Systems, Security, Evaluation, and Production Deplo
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Publisher: Independently Published
Pages: 740
Weight: 2.55
Height: 1.48 Width: 6.69 Depth: 9.61
Language: English
BUILDING RELIABLE AI AGENTS is a practical guide to designing, building, evaluating, securing, and deploying AI agents that can reason through tasks, use tools, retrieve information, maintain context, collaborate, and operate reliably in production.
Building an impressive AI prototype is one thing. Building an agent that can be trusted with real tasks, data, tools, and operational constraints is another.
This book shows you how to engineer reliable AI agent systems-from architecture and reasoning to security, evaluation, and production deployment.
You will learn how to:
- Design agent architectures with clear goals, state, and decision boundaries
- Connect models to tools and external systems using Model Context Protocol (MCP)
- Build Retrieval-Augmented Generation (RAG) systems for grounded decisions
- Design memory with persistence, context compression, and controlled forgetting
- Choose between ReAct, structured planning, tree search, and reflection
- Build reasoning loops that plan, act, observe, recover, and stop safely
- Design multi-agent systems with effective handoffs and coordination
- Defend against prompt injection and control permissions and sensitive assets
- Apply guardrails, human oversight, and safe action boundaries
- Evaluate reasoning, tool use, grounding, memory, and final outcomes
- Diagnose failures using traces, observability, and root-cause analysis
- Manage deployment, model routing, token costs, prompt caching, and context budgets
- Monitor reliability, performance, security, cost, and behavioral drift
Rather than treating AI agents as sophisticated prompts, this book treats them as engineered systems.
Learn to distinguish reasoning failures from retrieval problems, tool errors, context starvation, and coordination failures. Discover when a single agent is better than a multi-agent architecture and how to avoid unnecessary autonomy.
Written for software engineers, AI engineers, developers, architects, technical leaders, automation professionals, researchers, and serious AI practitioners, BUILDING RELIABLE AI AGENTS provides the architectural thinking and practical engineering principles required to build agents that can be evaluated, secured, monitored, and trusted.
Build agents for more than impressive demonstrations. Build them for reliable execution.
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