AI Agents and Automation: Build Reliable AI Workflows That Eliminate Repetitive Tasks, Save Time, Cut Costs, and Get Real Work Done

By Ethan Tyson

AI Agents and Automation: Build Reliable AI Workflows That Eliminate Repetitive Tasks, Save Time, Cut Costs, and Get Real Work Done

Problem

AI agents can generate impressive demos. The harder challenge is getting them to perform real work reliably, safely, and repeatedly.

If you are a developer, automation builder, technical professional, or business technologist, you may already know how to call an LLM. But what happens when that model must retrieve company data, choose tools, update business systems, survive API failures, request approval, control costs, and avoid unsafe actions?

Without the right architecture, AI automation can become unpredictable, expensive, difficult to debug, and risky to connect to real operations.

The goal is not maximum autonomy. It is dependable automation: systems that know when AI reasoning is useful, when deterministic software should stay in control, and when a human should make the final decision. AI_Agents_and_Automation_PRODUC...

Solution

AI Agents and Automation gives you a practical path from simple AI-assisted workflows to production-ready automation systems.

Rather than asking an LLM to control everything, you learn how to combine language-model reasoning with structured outputs, Python, APIs, business rules, tool calling, retrieval, memory, workflow orchestration, permissions, human approval, evaluation, and monitoring.

You will learn how to:

Turn unstructured requests into validated data and reliable automated workflows.Build tool-using agents that retrieve information and take controlled real-world actions.Connect AI automation to APIs, webhooks, n8n, RAG, memory, and Model Context Protocol (MCP).Add specialist agents only when multi-agent coordination genuinely improves the system.Evaluate, secure, deploy, monitor, and improve AI workflows after they reach production.

The book progresses deliberately from traditional automation and structured AI decisions to tools, business-system integrations, RAG, memory, n8n orchestration, MCP, multi-agent systems, evaluations, security controls, and production deployment. AI_Agents_and_Automation_PRODUC...

You do not need a machine-learning background, but basic Python knowledge will help you get the most from the hands-on examples. AI_Agents_and_Automation_PRODUC...

Proof

This is an implementation-focused book built around working systems rather than abstract discussions of autonomous AI.

You will create practical projects including structured task-routing workflows, tool-using customer-support agents, API-connected lead qualification systems, RAG-powered knowledge agents, memory-aware assistants, n8n invoice automation, MCP-enabled operations agents, and controlled multi-agent workflows. The later chapters add evaluations, tracing, prompt-injection defenses, permissions, human oversight, audit trails, durable execution, deployment, and production monitoring. AI_Agents_and_Automation_PRODUC...

Python serves as the primary implementation language alongside FastAPI, OpenAI tooling, Pydantic, databases, n8n, MCP, and standard HTTP integrations, with reliability patterns such as validation, retries, idempotency, structured outputs, approvals, and auditability built into the architecture. AI_Agents_and_Automation_PRODUC...

If you want to move beyond AI that merely produces answers and start building automation that completes useful work under clear operational controls, buy AI Agents and Automation and start engineering AI workflows you can actually put to work.

• Format: Paperback
• Language: English
• ISBN: B0HM49FT57
• ISBN13: 9798160555744
• Release Date: October 2026
• Publisher: Independently Published
• Length: 286 Pages
• Weight: 0.85 lbs.
• Dimensions: 0.6" x 6.0" x 9.0"