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Engineering Reliable AI Agents & Workflows: Move Beyond Demos: Architect Resilient, Production-Grade AI Systems
A Practical Guide for Teams Tired of AI Projects That Go NowhereSix months. $500K. And the AI system that "understood everything" in the demo can't tell a billing question from a shipping complaint in production.
This story plays out everywhere. Smart teams. Solid budgets. Reasonable expectations. And yet—failure.
You're not here because you don't understand AI. You're here because you've seen what happens when AI projects go wrong—and you're the one responsible for making sure yours doesn't.
What This Book Will Do For YouAfter reading this book, you will be able
Spot troubled projects early—before they consume six months and your credibilityAnswer "workflow or agent?" with confidence—using a decision framework, not gut feelPredict real costs accurately—so your $10K pilot doesn't become a $50K monthly surpriseDesign systems that fail gracefully—instead of failing mysteriously at 2 AMGet compliance sign-off faster—with governance patterns that satisfy legal and security teamsShip to production with confidence—because you've built for reliability, not just capabilityThis isn't about understanding AI better. It's about building AI systems that actually work when the demo ends and real users show up.
"Practical, grounded, and refreshingly anti-hype—perfect for technical leaders tired of 'AI fairy tales.'"— Hemanth Manda, Author, ex-IBM
The Core Insight
Companies treat AI agents as deterministic software when they are, in fact, probabilistic systems.
Traditional software is same input, same output, every time. AI systems don't work that way. The same prompt can produce different results. Context matters. Edge cases multiply. The behaviors that impressed everyone in the conference room become liabilities when real customers start using the system.
The gap between "the model can summarize text" and "AI reliably runs part of our business" isn't closed by better prompts. It's closed by better systems engineering.
What's InsideBefore You Build:The Post-Hype Diagnostic for Go/No-Go decisions. The Agent Litmus Test for "workflow or agent?" Token Economics for real cost modeling.
While You Architect: The Workflow-First Principle. Six Orchestration Capabilities every production system needs. State Management Patterns that prevent unpredictable behavior.
When You Ship: The Governance Triangle—three controls compliance won't reject. Seven diagnostic tools with worksheets you'll actually use.
Seven Diagnostic Frameworks • Post-Hype Diagnostic
• Agent Architecture Reality Check
• Agent Litmus Test
• Cost & Risk Assessment
• Human-In-The-Loop Integrity Check
• Governance & Data Boundary
• Evaluation Maturity Checks
Each with hands-on worksheets you'll actually use.
This Book Is For You If:• You've inherited an AI project that's behind schedule and over budget
• You're being asked to "add AI" without clear success metrics
• Your demos impress executives but operations can't run them
• You need to explain to lead
This story plays out everywhere. Smart teams. Solid budgets. Reasonable expectations. And yet—failure.
You're not here because you don't understand AI. You're here because you've seen what happens when AI projects go wrong—and you're the one responsible for making sure yours doesn't.
What This Book Will Do For YouAfter reading this book, you will be able
Spot troubled projects early—before they consume six months and your credibilityAnswer "workflow or agent?" with confidence—using a decision framework, not gut feelPredict real costs accurately—so your $10K pilot doesn't become a $50K monthly surpriseDesign systems that fail gracefully—instead of failing mysteriously at 2 AMGet compliance sign-off faster—with governance patterns that satisfy legal and security teamsShip to production with confidence—because you've built for reliability, not just capabilityThis isn't about understanding AI better. It's about building AI systems that actually work when the demo ends and real users show up.
"Practical, grounded, and refreshingly anti-hype—perfect for technical leaders tired of 'AI fairy tales.'"— Hemanth Manda, Author, ex-IBM
The Core Insight
Companies treat AI agents as deterministic software when they are, in fact, probabilistic systems.
Traditional software is same input, same output, every time. AI systems don't work that way. The same prompt can produce different results. Context matters. Edge cases multiply. The behaviors that impressed everyone in the conference room become liabilities when real customers start using the system.
The gap between "the model can summarize text" and "AI reliably runs part of our business" isn't closed by better prompts. It's closed by better systems engineering.
What's InsideBefore You Build:The Post-Hype Diagnostic for Go/No-Go decisions. The Agent Litmus Test for "workflow or agent?" Token Economics for real cost modeling.
While You Architect: The Workflow-First Principle. Six Orchestration Capabilities every production system needs. State Management Patterns that prevent unpredictable behavior.
When You Ship: The Governance Triangle—three controls compliance won't reject. Seven diagnostic tools with worksheets you'll actually use.
Seven Diagnostic Frameworks • Post-Hype Diagnostic
• Agent Architecture Reality Check
• Agent Litmus Test
• Cost & Risk Assessment
• Human-In-The-Loop Integrity Check
• Governance & Data Boundary
• Evaluation Maturity Checks
Each with hands-on worksheets you'll actually use.
This Book Is For You If:• You've inherited an AI project that's behind schedule and over budget
• You're being asked to "add AI" without clear success metrics
• Your demos impress executives but operations can't run them
• You need to explain to lead
255 pages, Kindle Edition
Published December 6, 2025
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