finelybook
Building Agentic AI Systems: Build connected agent systems with MCP, A2A, agent loops, harness engineering, and cloud deployment 2nd Edition
Author(s): Anjanava Biswas (Author), Wrick Talukdar (Author)
- Publisher Finelybook 出版社: Packt Publishing
- Publication Date 出版日期: October 28, 2026
- Edition 版本: 2nd
- Language 语言: English
- Print length 页数: 440 pages
- ISBN-10: 1807302539
- ISBN-13: 9781807302535
Book Description
Go beyond standalone agents. Build interoperable, production-ready agent systems with MCP, A2A, agent loops, harness engineering, evaluation, observability, safety, and enterprise cloud deployment.
Key Features
- New in the second edition: connect agents with MCP, A2A, and modern multi-agent frameworks
- Engineer reliable agent loops and harnesses for memory, tools, context, retries, and guardrails
- Evaluate, secure, observe, and deploy agents across Kubernetes and managed AI platforms
Book Description
The first edition of Building Agentic AI Systems showed how autonomous agents reason, plan, reflect, use tools, and collaborate. This second edition tackles the next challenge: connecting agents across tools, frameworks, and platforms, then engineering the loops, harnesses, and infrastructure for reliable production.
You’ll strengthen the agent stack with memory, context engineering, reasoning and recovery loops, and harness engineering, then move into the protocols reshaping agentic AI. Implement Model Context Protocol (MCP) for tool and data access, use A2A for capability discovery and cross-agent collaboration, and understand where it fits for asynchronous agent communication.
New coverage brings these standards together with LangGraph, CrewAI, Google ADK, and Strands. You’ll design multi-agent workflows and deploy distributed systems with Kubernetes, Amazon Bedrock AgentCore, and Google Vertex AI Agent Engine while addressing tracing, debugging, cost, reliability, and trust boundaries.
You’ll also evaluate agents with AgentBench, GAIA, SWE-bench, and production metrics, apply human-in-the-loop controls and governance, and assess where Small Language Models can reduce latency and cost. By the end, you’ll build connected agent systems that are interoperable, observable, secure, measurable, and ready to scale.
What you will learn
- Design interoperable multi-agent systems with MCP and A2A
- Engineer agent loops for reasoning, reflection, tools, and recovery
- Build harnesses for context, memory, tool access, and guardrails
- Integrate LangGraph, CrewAI, ADK, Strands, and agent frameworks
- Deploy distributed agents on Kubernetes and managed AI platforms
- Evaluate agents with AgentBench, GAIA, SWE-bench, and tracing
- Apply HITL, governance, safety controls, and trust boundaries
- Use SLMs for lower latency, edge deployment, and cost control
Who this book is for
This book is for software developers, AI engineers, enterprise and solution architects, platform engineers, and technical leaders building production agentic systems. It is ideal for readers of the first edition who want to move from standalone agents to interoperable multi-agent architectures, agent loops, harness engineering, modern protocols, evaluation, and cloud deployment. Familiarity with Python, APIs, LLM applications, or basic agent concepts is helpful.
Table of Contents
- Foundations of Agentic AI
- Agent Cognitive Architecture: Memory, Context, and Reasoning
- Agent Harnesses and Execution Loops
- The Interoperability Imperative: Why Standards Matter
- Understanding the MCP Architecture and Implementation
- MCP in Practice: Security, Deployment, and Case Studies
- A2A Protocol Fundamentals: Agent Cards and Capability Discovery
- Building with A2A: Multi-Agent Systems and Framework Integration
- Deploying Distributed Agent Systems
- Agent Evaluation Frameworks and Benchmarking
- Responsible AI, Safety, and Ethics for Agents
- Small Language Models for Agentic Systems
- The Future of Agentic AI
Editorial Reviews
Editorial Reviews
About the Author
Anjanava Biswas is an award-winning senior Applied AI engineer and architect at Google, with over 18 years of industry experience. Specializing in machine learning, Generative AI, natural language processing, deep learning, data analytics, and cloud architecture, he partners with large enterprises to build and scale advanced AI systems in the cloud. Anjanava is widely recognized for his contributions to the field of applied AI. He has published research in multiple scientific journals and actively contributes to open-source AI/ML projects. His professional accolades include Fellowships with BCS (UK), the IET (UK), and IETE (India), and he is a senior IEEE member. A frequent public speaker, Anjanava has held key positions at industry giants like AWS, IBM and Oracle Corp. Originally from India, he now resides in San Diego, CA, with his wife and son, where he continues to innovate and inspire within the tech community.
Wrick Talukdar is a technology leader in generative AI at Amazon, with over two decades of global experience across AI, cloud computing, and product leadership. He has led large-scale modernization initiatives that drive enterprise growth and reach millions of users worldwide and has spearheaded the productization of award-winning AI/ML technologies now deployed at scale across Fortune 500 companies.
His research spans generative AI, multimodality, speech, and computer vision, and is widely cited and referenced in the field. Wrick serves as Chair of Technology and Innovation for the Industry Engagement Committee and as a Senior Technical Committee Member of the IEEE Machine Learning, Deep Learning and AI in Consumer Electronics and the Industry AI Labs(IEC). Across these roles he helps shape technical standards, drives research and innovation in next-generation products and solutions, and influences the future direction of safe AI and its adoption, while mentoring young professionals entering the field.
