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The End of 'It Hallucinated'

Stop Saying 'The AI Messed Up'

The 6 Specific Failure Patterns

Memory and Focus: Context Degradation & Specification Drift

People-Pleasing and Wrong Tools: Sycophantic Confirmation & Tool Selection Errors

The Domino Effect and The Invisible Threat: Cascading & Silent Failures

Fixing the Root Cause

Structural Fixes for Every Failure Type

The Failure Diagnosis Prompt

Quiz: Diagnosing AI Failures

Recognizing the 6 Failure Types

The Danger of Silent Failure

Diagnosing Bad Output

Applying Structural Fixes

Using the Diagnosis Prompt

Moving Beyond 'Hallucination'

Course Wrap-Up and Feedback

Module 5 Course Survey

Are you tired of hearing 'the AI hallucinated' every time an agent workflow breaks down. In the rapidly evolving world of artificial intelligence, treating AI as a mysterious black box is no longer acceptable.

Are you tired of hearing 'the AI hallucinated' every time an agent workflow breaks down? In the rapidly evolving world of artificial intelligence, treating AI as a mysterious black box is no longer acceptable. 'AI Agent Failure Patterns: Diagnosis and Structural Fixes' is a comprehensive course designed to transform how you evaluate, diagnose, and repair AI agent systems. This course moves beyond generic catch-all terms like 'glitch' or 'hallucination' and equips you with a precise, engineering-grade vocabulary to identify exactly why an AI agent failed. You will dive deep into the 6 specific failure patterns plaguing modern AI workflows: Context Degradation, Specification Drift, Sycophantic Confirmation, Tool Selection Errors, Cascading Failures, and the highly dangerous Silent Failures. Through real-world examples and systematic analysis, you will learn how to spot these errors before they impact downstream business processes. But diagnosis is only half the battle. This course provides actionable, structural fixes for every failure type—from memory compression and guardrails to circuit-breakers and optimized tool descriptions. You will also master the 'Failure Diagnosis Prompt' to run blameless post-mortems and provide clear, actionable feedback to engineering teams. Whether you are an AI engineer, product manager, or QA specialist, this course will empower you to build robust, reliable, and production-ready AI agents. Stop guessing why your AI failed, and start engineering structural solutions today.

Module 5: AI Agent Failure Patterns: Diagnosis and Structural Fixes

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            <p>We’ve all been in the room when an AI agent produces a bizarre result, only for someone to shrug and say, "It just hallucinated." That phrase has become the ultimate conversation-stopper, a way to treat sophisticated software like a mysterious black box that occasionally throws a tantrum. But if you’re building, managing, or buying these systems, "hallucination" isn't a diagnosis—it’s an admission that you don’t know what actually went wrong. This course is about moving past those lazy catch-all terms and developing the technical vocabulary to pinpoint exactly where an agent lost the plot. Whether you're a hiring manager trying to spot a candidate who has actually wrestled with production-grade AI or a developer tired of playing whack-a-mole with unpredictable bugs, you need to stop treating AI errors as random glitches and start seeing them as specific, identifiable failure patterns.</p>

<p>We are going to break down the six specific ways AI agents actually fail, from "Context Degradation," where the agent loses its way due to token limits, to the "Silent Failure," where the output looks perfectly confident but is fundamentally fabricated. You’ll learn how to spot "Sycophantic Confirmation"—the AI’s tendency to agree with your flawed premises just to be a people-pleaser—and how to map out "Cascading Failures" where one tiny error in a multi-step workflow compounds into a disaster. More importantly, we aren't just identifying problems; we’re implementing structural fixes. We’ll cover how to build circuit-breakers, design better tool descriptions, and use a standardized failure diagnosis prompt to turn a broken output into a clear engineering ticket. By the end, you’ll have the framework to run a blameless post-mortem and the confidence to build agents that are actually reliable enough for the real world.</p>
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