What I Learned Running AI Agents in Production for Six Months

Six months ago, we deployed our first autonomous AI agent to handle customer support ticket routing. The demo was flawless. Production was a different story.

Here's what I learned about the reality of running AI systems that make decisions without human oversight.

The Demo-to-Production Gap is Enormous

Our routing agent worked perfectly in testing. It correctly categorized support tickets 95% of the time, routed urgent issues to the right teams, and even drafted initial responses.

In production, it:

• Misclassified a billing dispute as a feature request
• Routed a security incident to the marketing team
• Generated a cheerful response to an angry enterprise customer threatening to cancel

Lesson: Test with real data, real edge cases, and real consequences. Your carefully curated test dataset won't prepare you for what customers actually send.

Error Handling Becomes Critical

Traditional software fails predictably. AI systems fail creatively.

What We Built

Ticket → AI Classification → Route to Team

What We Should Have Built

Ticket → AI Classification → Confidence Check → Human Review (if low confidence) → Route to Team
                          ↓
                    Fallback Queue

Key insight: Always include confidence scores and fallback mechanisms. When the AI is uncertain, default to human review.

Monitoring AI Systems is Different

Traditional monitoring focuses on uptime and response times. AI monitoring requires tracking:

• Accuracy drift: Performance degrades over time as data patterns change
• Confidence distribution: Are you getting more low-confidence predictions?
• Edge case frequency: How often are you hitting scenarios the model wasn't trained on?
• Human override rates: When do operators intervene?

Our Monitoring Dashboard

• Daily accuracy by ticket type
• Confidence score histogram
• Human review queue length
• Top misclassification patterns

The Human-in-the-Loop Reality

"Autonomous" is misleading. Even our most automated systems require human oversight.

What Actually Happens

1. Morning review: Operations team checks overnight decisions
2. Exception handling: Humans step in for edge cases
3. Weekly calibration: Adjust thresholds based on performance
4. Monthly retraining: Update models with new data patterns

Budget for human time. Your "autonomous" system will need dedicated operators.

Data Quality Matters More Than Model Quality

We spent weeks fine-tuning our model architecture. The biggest improvement came from cleaning our training data.

Before Data Cleanup

• Accuracy: 78%
• Common errors: Mixing up "billing" and "technical support"

After Data Cleanup

• Accuracy: 91%
• Removed duplicate tickets with conflicting labels
• Standardized category definitions
• Added examples for edge cases

Invest in data quality first. A simpler model with clean data beats a complex model with messy data.

Gradual Rollout is Essential

Our Rollout Strategy

Week 1-2: Shadow mode (AI makes predictions, humans make decisions) Week 3-4: 10% of tickets routed by AI Week 5-8: 50% of tickets Week 9+: 90% of tickets (keeping 10% for human review)

Why This Worked

• Caught edge cases early
• Built operator confidence
• Identified training gaps
• Established monitoring baselines

The Cost Reality

Running AI in production isn't just about API costs.

Our Monthly Costs

• Model API calls: $400
• Monitoring infrastructure: $200
• Human oversight (20 hours/week): $2,000
• False positive cleanup: $300

Total: $2,900/month for a system that saves ~60 hours of manual ticket routing

What I'd Do Differently

1. Start with stricter confidence thresholds: Better to route too many tickets to humans than misroute important ones
2. Build monitoring first: Don't deploy without comprehensive tracking
3. Plan for model updates: Your first model won't be your last
4. Document everything: Edge cases, decisions, and failure modes
5. Train operators thoroughly: They need to understand when and how to intervene

The Bottom Line

Autonomous AI operations work, but they're not autonomous. They're sophisticated tools that require careful integration, constant monitoring, and human oversight.

The value isn't in replacing humans—it's in augmenting them. Our routing agent now handles 90% of straightforward cases, letting our team focus on complex customer issues that require real judgment.

Start small, monitor everything, and remember: the goal isn't perfect automation. It's reliable assistance.