What AI Agents Are Bad At (And Why)

Описание: AI agents look autonomous and powerful—but in practice, they’re surprisingly fragile.

In this video, we break down what AI agents are bad at, why those failures keep showing up in real systems, and why the root cause is usually engineering—not intelligence. You’ll see how planning drift, tool misuse, memory limits, and missing guardrails turn impressive demos into brittle automation.

Rather than focusing on hype or tools, we look at how agentic systems actually behave in production:
Why agents lose the thread in long-running tasks
How small tool mismatches cascade into major failures
Why bigger models and larger context windows don’t magically fix reliability
And which guardrails—validation, constraints, and human oversight—actually work

If you’re building or evaluating AI agents, this video will help you understand where they struggle today and how to design systems that fail less often.

This video is part of the Enginerds Fundamentals series—clear, calm explanations of the technologies shaping modern software and systems.

CHAPTERS

0:00 – Introduction
1:26 – Agents Lack True Understanding
2:57 – The Surprising Power of the Loop
4:25 – Lost Assumptions in Long Tasks
5:55 – Unverified Automation Risks
7:25 – Why Schema Validation Matters
8:55 – Schema Validation for Tools
10:25 – Limits of Context Windows
11:54 – From Demo to Real Systems
13:22 – Guardrails for Communication and Payments
14:50 – Building Reliable AI Systems

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