Lesson 1560 of 1570
How AI Agents Fail (And How to Catch Them)
The specific ways agents go wrong and the habits that catch them early.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The big idea
- 2failure mode
- 3loop
- 4logging
Concept cluster
Terms to connect while reading
Section 1
The big idea
AI agents fail in predictable patterns: getting stuck in loops, taking the wrong action confidently, hallucinating tools they don't have, ignoring safety instructions when pressured. Knowing the common failure modes helps you build in checks before they bite you.
Some examples
- Loops: agent keeps trying the same failing action — set a max-attempts limit.
- Confident wrong actions: agent picks something plausible but incorrect — check work before commit.
- Tool hallucination: agent uses a function that doesn't exist — limit to a defined toolset.
- Goal drift: agent quietly switches to easier sub-goal — restate the real goal in checks.
Try it!
Set up logging for one agent task this week. Read the log on Sunday and find one surprise.
Key terms in this lesson
End-of-lesson quiz
Check what stuck
15 questions · Score saves to your progress.
Tutor
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