Lesson 1273 of 2116
Designing Agents That Fail Gracefully When a Tool Breaks
How agents should react when a tool returns 500, times out, or returns garbage.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2graceful-degradation
- 3tool-failure
- 4retries
Concept cluster
Terms to connect while reading
Section 1
The premise
An agent that retries blindly burns money; one that classifies the failure and adapts is production-ready.
What AI does well here
- Distinguish transient (retry), permanent (give up), and ambiguous (escalate) failures
- Backoff with jitter on transient errors
- Fall back to a degraded but useful answer when a tool is down
- Tell the user clearly what was missing from the answer
What AI cannot do
- Know whether a retry will succeed without trying it
- Recover credentials it lost mid-run
- Decide which fallback is acceptable without your stated preferences
Key terms in this lesson
End-of-lesson quiz
Check what stuck
15 questions · Score saves to your progress.
Tutor
Curious about “Designing Agents That Fail Gracefully When a Tool Breaks”?
Ask anything about this lesson. I’ll answer using just what you’re reading — short, friendly, grounded.
Progress saved locally in this browser. Sign in to sync across devices.
Related lessons
Keep going
Creators · 48 min
Computer Use API: Letting AI Click Through GUIs
Computer Use lets Claude see your screen and use it — mouse, keyboard, apps. The capability is real, the gotchas are real. A hands-on look at what works in 2026.
Creators · 45 min
Browser Agents: Capabilities and Pitfalls
Browser agents — Operator, Atlas, Browser Use, MultiOn — are the most visible agent category. The capability is genuine, the failure modes are specific. Build with eyes open.
Creators · 52 min
Production Agent Patterns: Queues, Retries, Idempotency
A prototype agent and a production agent have the same LLM. What's different is everything around it — durable state, retries, idempotency, observability. The real engineering.