AI Agents Fail for One Boring Reason

Описание: Most “AI agents” don’t fail because the model is dumb.
They fail because the system is missing the boring parts: structure, boundaries, verification, and logs.

In this Fundamentals deep dive, you’ll learn what an AI agent actually is (a loop that plans, uses tools, checks results, and stops safely), why “just add tools” breaks in production, and how to build bounded autonomy—the “box” that makes agents reliable inside real organizations.

We’ll also cover why retrieval + summarization isn’t enough once outputs drive actions, and how structured claims (LAW / SCOPE / EVIDENCE) turn language into operational knowledge you can validate.

If you’re building agents—or deciding whether to trust them—this is the systems blueprint from demo to deployment.

New videos weekly.

CHAPTERS:
0:00 - Introduction
1:10 - Typical AI Agent Requests
2:21 - Model vs. Pipeline
3:32 - Risks of Merged AI Outputs
4:43 - Design Move: Structured Claims
5:52 - Boundaries and Definitions
7:02 - Parts of the Autonomy Box
8:12 - Explainability in Production
9:23 - Fixing Missing Context
10:32 - Tracking Performance Consistently
11:42 - Making AI Systems Governable

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