Course Objective & Scope
A workflow that looks flawless in a demo can double-charge a customer or silently drop records in production when an API times out, an LLM returns malformed JSON or step 4 of 5 fails. This course teaches the engineering patterns required to make automation and AI workflows resilient in production.
Participants practice separating transient network/quota errors from permanent business errors, enforcing idempotency across retries, repairing malformed LLM outputs, falling back to secondary models and replaying failed payloads from a Dead-Letter Queue (DLQ).
Who Should Attend?
- Automation builders and architects running business-critical production workflows
- Integration, platform and SRE/DevOps engineers
- Technical teams building multi-step LLM and agent pipelines
- Prerequisite: hands-on experience building API-driven workflows
Key Takeaways
- Classify failures into transient infrastructure errors, permanent payload errors, LLM schema errors and business exceptions
- Configure exponential backoff with jitter, rate-limit throttling and circuit breakers
- Guarantee exactly-once business effects across retries using idempotency keys and state tables
- Recover from malformed LLM outputs or provider outages with repair prompts and model fallbacks
- Design compensating actions (Saga pattern) and Dead-Letter Queue (DLQ) replay workflows
Curriculum
Failure Taxonomy: Deciding What to Retry
- Transient failures (HTTP 429, 502, 503, timeouts) vs permanent errors (400, 401, 422 business rules)
- How blind retries trigger retry storms and cascading outages
Retry Policies, Backoff and Circuit Breakers
- Exponential backoff with jitter and honouring
Retry-Afterheaders - Circuit breakers and queue buffering when downstream APIs degrade
Idempotency and Handling Partial Failures (Saga Pattern)
- Preventing duplicate emails, invoices or records when a flow re-runs
- Compensating transactions when step N of a multi-system flow fails
LLM-Specific Failures: Schema Repair and Model Fallbacks
- Handling malformed JSON, truncated responses and safety-filter refusals
- Self-healing validation retries and failing over to a secondary model/provider
Dead-Letter Queues (DLQ), Alerting and Replay Operations
- Persisting unrecoverable payloads with full context into a DLQ
- Safe single-item and bulk replay workflows after fixing the root cause
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