Error Handling & Reliability

Production workflows need to handle the real world — where APIs go down, data is messy, and AI sometimes returns nonsense. This lesson covers how to build workflows that recover gracefully.

The Three Types of Failures

1. Transient Failures

Temporary issues that fix themselves:

• •API rate limit hit (too many requests)
• •Network timeout
• •Service temporarily unavailable

Solution: Retry with exponential backoff

• •Attempt 1: Wait 1 second, retry
• •Attempt 2: Wait 5 seconds, retry
• •Attempt 3: Wait 30 seconds, retry
• •Give up after 3 attempts and alert

2. Data Failures

The input data is invalid or unexpected:

• •Missing required fields
• •Wrong data format (text where a number was expected)
• •Unexpected characters or encoding

Solution: Validate before processing

IF email field is empty → Skip this record, log "Missing email"
IF amount is not a number → Flag for human review
IF text contains > 100,000 characters → Truncate with warning

3. AI Failures

The AI returns something unexpected:

• •Hallucinated data
• •Wrong output format (prose instead of JSON)
• •Refused to answer (safety filter triggered)
• •Irrelevant response

Solution: Validate AI output

IF AI response is not valid JSON → Retry with stricter prompt
IF classification not in allowed list → Default to "other"
IF confidence score < 50% → Route to human review
IF response is empty → Retry once, then flag

Building Robust Error Handling

The Guard Pattern

Before every AI step, add a validation step:

Step 1: Receive data
Step 2: GUARD - Validate required fields exist
  ↳ IF invalid → Log error, send alert, stop
Step 3: AI Classification
Step 4: GUARD - Validate AI returned expected format
  ↳ IF invalid → Retry with stricter prompt
  ↳ IF still invalid → Route to human review
Step 5: Continue workflow

The Dead Letter Queue

When a record fails all attempts at processing, don't lose it:

1. 1.Save the failed record to a "dead letter" spreadsheet or database
2. 2.Include: the original data, which step failed, the error message, timestamp
3. 3.Review and reprocess dead letters weekly

This ensures no data is ever silently lost.

Idempotency

If a workflow runs twice with the same input (which happens with retries), it should produce the same result without duplicates.

How to achieve this:

• •Check if a record already exists before creating it
• •Use unique IDs to detect duplicate processing
• •Design actions that are safe to repeat (update instead of insert)

Alerting and Notifications

Your workflow should tell you when something goes wrong:

Alert Levels

What to Include in Alerts

• •Which workflow failed
• •Which step failed
• •The input data that caused the failure
• •The error message
• •How many times this error has occurred recently
• •Suggested fix (if known)

Monitoring Dashboard

For production workflows, track these metrics:

• •Success rate: % of runs that complete without errors
• •Average execution time: Is the workflow getting slower?
• •Error frequency by step: Which step fails most often?
• •Retry rate: How often are retries needed?
• •AI cost per run: Are you staying within budget?
• •Dead letter queue size: Are unprocessed records piling up?

Most automation platforms have built-in monitoring. For custom solutions, log to a Google Sheet or a simple dashboard.