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Engineering AI Agents for Production: Infrastructure, Observability, Security, and Production Case Studies
Paperback
Series: From Llms to Agent Ecosystems, Book 2
Publisher: Independently Published
Published: Apr 26 2026
Pages: 830
Weight: 3.11
Height: 1.65 Width: 7.00 Depth: 10.00
Language: English
Every pattern, protocol contract, and orchestration design in Volume 1 was a specification. Engineering AI Agents for Production implements them-in production-grade Java, at distributed scale, under real operational constraints.
This is not a theory extension. It is a construction manual for engineers who are shipping autonomous AI systems into production: building the runtime from scratch, deploying across distributed infrastructure, measuring quality with engineering rigor, hardening against novel threats, and operating systems whose failure modes are non-deterministic and sometimes irreversible.
What this book builds - 828 pages, 31 chapters, 6 parts:- Part I - A Production Java Agent Framework, from Scratch: Design principles before code (testability and observability as first-class constraints), Java 25 concurrency models (structured concurrency and Virtual Threads), the AgentRuntime as a deterministic state machine, the AgentExecutorService, and the full protocol adapter stack for MCP and A2A.
- Part II - Infrastructure for Distributed Agent Systems: The agent control plane (service discovery and health checking), agent sandboxing with Docker, gVisor, and eBPF, WebAssembly (WASI 2.0) for edge agents, event-driven architectures with Kafka and NATS, and durable execution with Temporal and AWS Step Functions.
- Part III - Observability, Evaluation, and Testing: The three observability pillars (distributed tracing, structured logging, metrics), evaluation as an engineering discipline (trajectory quality scoring, LLM-as-judge pipelines), chaos engineering for non-deterministic systems, and deterministic replay and time-travel debugging.
- Part IV - Security and Governance: The complete agent threat landscape (prompt injection, tool poisoning, denial-of-wallet), secure-by-design principles (least privilege, secure bootstrapping), enterprise governance (RBAC, audit logging, Policy-as-Code with OPA), and runtime circuit breakers and kill switches.
- Part V - Eight Production Architecture Case Studies: Detailed breakdowns including architecture decisions and production failure modes. Covers: Enterprise Copilot (M365/Google integration), Autonomous Research Agent, DevOps and SRE Agent, Multi-Agent Customer Support, Autonomous Code Review, Regulated Clinical/Financial Agent, and Decentralised Fraud Detection.
- Part VI - The Future of Agentic AI: The agent as a first-class software entity (identity, accountability, versioning), coordination horizons and emergent behavior at scale, the convergence of trust and delegation standards, and the frontier of agent regulation and liability.
- Java and Python engineers implementing production agent runtimes from scratch.
- Platform and Infrastructure engineers deploying agent workloads on Kubernetes and distributed event systems.
- ML Engineers building evaluation, replay, and cost-governance infrastructure for non-deterministic systems.
- Security Architects extending enterprise threat models and governance frameworks to autonomous AI systems.
- Engineering Leads who need real-world case studies-including failure modes-that map to their own deployment context.
Volume 2 requires familiarity with the agentic loop, capability stack, and the MCP/A2A protocol layer-all established in Volume 1 or equivalent engineering experience. The implementation language throughout Part I is Java 25 LTS. Parts II-VI are language-agnostic.
Architecture is the set of decisions that are hard to reverse. Make them deliberately.
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