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Durable AI Agents with Temporal: Build Reliable Production Agent Workflows in Python with MCP, Human Approval, Retries, Testing, and Deployment
Paperback
Publisher: Independently Published
Published: Aug 7 2026
Pages: 274
Weight: 1.06
Height: 0.58 Width: 7.00 Depth: 10.00
Language: English
Building an AI agent is exciting. Keeping it reliable when APIs fail, workers restart, approvals take hours, or tools run twice is the real challenge.
Durable AI Agents with Temporal shows you how to build AI workflows that are designed to survive real production conditions.
You do not need prior experience with Temporal, MCP, durable workflow engines, or distributed systems. With basic familiarity with Python and APIs, you can follow the book step by step as you build a practical Durable Research-to-Action Operations Agent from the ground up.
Instead of overwhelming you with theory or huge code listings, the book introduces each concept in manageable stages. You will run the system, inspect what happens, test failures, fix problems, and build confidence through small, working milestones. Mistakes are treated as part of the learning process, not something to fear.
Key FeaturesPractical, step-by-step introduction to Temporal durable execution
Reliable LLM and agentic AI workflow orchestration
Retrieval and Model Context Protocol (MCP) integration
Durable human-in-the-loop approval
Retries, timeouts, checkpoints, and long-running workflows
Idempotency and protection against duplicate side effects
Saga compensation for recovering partial operations
Testing, replay verification, and failure injection
Security, prompt-injection protection, auditing, and observability
Docker, CI/CD, Temporal Cloud, Worker Versioning, and rollback
You will learn how to:
Separate deterministic Workflow logic from external Activities
Build AI agents that recover after worker and dependency failures
Generate and validate structured LLM plans
Integrate retrieval and governed MCP tools
Pause safely for human approval and resume hours or days later
Manage Workflow state, agent context, and long-running operations
Prevent duplicate external actions
Test failures before they happen in production
Monitor logs, metrics, traces, model usage, and cost
Deploy and operate reliable AI-agent systems
This book is ideal for Python developers, AI application builders, students, self-learners, software engineers, and professionals who want to understand production AI systems without needing previous Temporal or distributed-systems experience.
Table of ContentsChapter 1: Engineering AI Agents for Real Production Conditions
Chapter 2: Workflows, Activities, Workers, and Durable Recovery
Chapter 3: Building Reliable LLM Planning and Execution
Chapter 4: Retrieval, Tools, and MCP Integration
Chapter 5: Workflow State, Agent Memory, and Long-Running Execution
Chapter 6: Human Approval and Resumable Agent Workflows
Chapter 7: Idempotency, Side Effects, and Compensation
Chapter 8: Testing and Evaluating Durable AI Agents
Chapter 9: Securing, Auditing, and Observing Durable Agents
Chapter 10: Docker, CI/CD, Deployment, and Production Operations
Stop building AI agents that only work when everything goes right. Start building systems that can recover when things go wrong.
Begin Durable AI Agents with Temporal today and turn fragile AI prototypes into reliable, production-ready workflows-one practical step at a time.
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