Lesson 1845 of 2116
AI and agent retry and backoff strategy
Decide what to retry, how often, and when to give up — agents that retry forever waste money and miss real failures.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2Designing Retry Policies for Flaky Agent Tools
- 3The premise
Concept cluster
Terms to connect while reading
Section 1
The premise
Retries are useful for transient errors and dangerous for everything else. A clear policy beats ad-hoc loops.
What AI does well here
- Classify errors as transient vs permanent.
- Propose backoff curves (exponential, jittered).
- Identify operations that must be idempotent before retry.
What AI cannot do
- Know which APIs are safe to retry without idempotency keys.
- Replace circuit breakers for upstream outages.
- Reason about retry storms across many agents.
Section 2
Designing Retry Policies for Flaky Agent Tools
Section 3
The premise
Agents that retry every error get stuck; agents that retry nothing fail on transient errors. The right policy distinguishes between the two.
What AI does well here
- Retry a clearly transient error (timeout, 503) with backoff.
- Escalate a structural error (404, auth) to the human.
What AI cannot do
- Always tell which class an error belongs to from one sample.
- Decide that an external system is permanently down.
Key terms in this lesson
End-of-lesson quiz
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