engineering · 1 min read
Error Handling and Retries in AI Workflows
AI workflows fail more often than CRUD apps. Here is how Bhogar AI handles errors, retries and dead-letter queues so production stays calm.
BABhogar AI TeamProduct & Engineering
AI workflows fail in more interesting ways than CRUD apps: rate limits, hallucinations, schema drift, model downtime. A retry strategy designed for HTTP 500s is not enough.
Why it matters
Naive retry-on-everything makes problems worse. Production workflows need typed error handling: retry transient errors, escalate semantic errors, and dead-letter the rest.
How Bhogar AI approaches it
Bhogar AI ships typed error categories (transient, rate-limited, semantic, fatal) with default policies per category, plus per-node overrides. Dead-lettered runs land in a queue you can inspect and replay.
- Typed error categories with default policies
- Exponential backoff with jitter and Retry-After honouring
- Dead-letter queue with replay UI
- Circuit breakers per integration
- Alerting and dashboards per error type
What you get
Customers using typed retries see 50-70% reduction in pager incidents from transient errors and faster recovery on the rest.