Error Handling Patterns for AI Workflows

AI workflows fail. APIs timeout, models hallucinate, data is malformed. The difference between a toy and a production system is how it handles errors.

⚠️ Reality Check

Every production AI workflow will encounter errors. Plan for them from day one.

Common AI Workflow Errors

🔌 API Errors

Rate limiting — Too many requests too fast
Timeouts — API didn't respond in time
Authentication — Expired or invalid credentials
Server errors — 500-level responses

🤖 AI Model Errors

Invalid output — Doesn't match expected schema
Refusal — Model declines to answer
Hallucination — Plausible but incorrect output
Context overflow — Input too long

📊 Data Errors

Missing fields — Expected data not present
Type mismatch — String when expecting number
Encoding issues — Unicode, special characters
Empty responses — API returns nothing

Error Handling Patterns

Pattern 1: Retry with Exponential Backoff

For transient errors (rate limits, timeouts), retry with increasing delays:

● Attempt 1: immediate

● Attempt 2: wait 1 second

● Attempt 3: wait 2 seconds

● Attempt 4: wait 4 seconds

● Attempt 5: fail permanently

Pattern 2: Fallback to Alternative

When primary option fails, use backup:

Primary AI model fails → Use fallback model

API unavailable → Use cached response

Complex prompt fails → Try simpler version

Pattern 3: Graceful Degradation

Return partial results rather than complete failure:

{
  "success": "partial",
  "processed": 95,
  "failed": 5,
  "results": [...],
  "errors": [
    { "item": 23, "error": "timeout" },
    { "item": 45, "error": "rate_limit" }
  ]
}

Pattern 4: Circuit Breaker

When errors exceed threshold, stop calling failed service:

Track error rate over time window
If rate > threshold, open circuit
Reject requests immediately (fail fast)
Periodically test if service recovered
If recovered, close circuit and resume

Implementing in Workflows

Use Condition Nodes for Routing

Check for errors and route accordingly:

AI Prompt Output
        ↓
Condition: Is Valid?
   ↓ Yes        ↓ No
✓ Continue
→ Retry Logic

Validate AI Output

Always validate AI responses against expected schema:

Required fields present
Values within expected ranges
Format matches specification

Log Everything

Comprehensive logging enables debugging:

Input that caused the error
Full error message and stack trace
Retry attempts and outcomes
Final resolution (success, fallback, failure)

Testing Error Handling

Deliberately trigger errors to verify handling:

Use invalid API keys to test auth errors
Send malformed data to test validation
Exceed rate limits to test throttling
Use chaos testing for random failures

Alerting and Monitoring

Set up alerts for:

Error rate exceeds threshold
Specific critical errors occur
Retry queue grows too large
Circuit breaker opens

Build Resilient Workflows

Evaligo provides built-in error handling, retry logic, and monitoring. Focus on your business logic while the platform handles reliability.