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 classification. Today, it processes 40,000+ tickets monthly with 94% accuracy. Here's what we learned about running AI operations in the real world.

The Promise vs. Reality

The sales pitch was compelling: an AI agent that could classify support tickets, route them to appropriate teams, and even draft initial responses. The reality was messier.

What worked:

Ticket classification improved from 78% to 94% accuracy
Average response time dropped from 4 hours to 12 minutes
Support team could focus on complex issues instead of triaging

What didn't:

Edge cases broke the system regularly
Cost spiraled during the first month
Customer satisfaction initially dropped due to impersonal responses

Lesson 1: Start with Constrained Domains

Our biggest early mistake was giving the agent too much autonomy. We started with full ticket handling—classification, routing, and response generation.

The fix: We scaled back to classification only for the first month, then gradually added capabilities.

Implementation Strategy

Week 1-4: Classification only (5 categories)
Week 5-8: Add routing to specific team queues
Week 9-12: Draft responses for simple issues
Month 4+: Handle end-to-end resolution for common problems

This staged approach let us identify failure modes early and build robust error handling.

Lesson 2: Monitoring is Everything

Traditional application monitoring doesn't work for AI agents. We needed entirely new observability approaches.

Critical Metrics We Track

Model Performance:

Confidence scores distribution
Classification accuracy by category
Response time percentiles
Token usage and costs

Business Impact:

Customer satisfaction scores
Escalation rates
Time to resolution
Agent workload reduction

System Health:

API error rates
Timeout frequencies
Fallback trigger rates
Queue depth

Early Warning System

We built alerts for:

Confidence scores dropping below 0.7 for >10% of requests
Classification accuracy falling below 90% over 1-hour windows
Cost exceeding $500/day (our normal is $200/day)
More than 5% of tickets hitting fallback handlers

Lesson 3: Human-in-the-Loop Isn't Optional

We initially tried full automation. Big mistake.

The Incident

In week 3, the agent misclassified a series of billing complaints as "feature requests." Customers waited days for responses while the product team wondered why they were getting so many billing questions.

Root cause: The model hadn't seen enough examples of customers describing billing issues in frustrated language.

Our Solution: Smart Escalation

Now we escalate to humans when:

Confidence score < 0.8
Customer uses words like "frustrated," "angry," or "cancel"
Ticket mentions billing, refunds, or account issues
Response length exceeds 200 words (usually indicates complexity)

Result: 15% of tickets get human review, but customer satisfaction recovered to pre-AI levels.

Lesson 4: Cost Control Requires Active Management

Our first month's bill was $3,200 instead of the projected $800.

What Went Wrong

No token limits per request
Retry logic that created infinite loops
Development environment hitting production APIs
Inefficient prompt engineering (too verbose)

Cost Optimization Strategies

Prompt Engineering:

Reduced average prompt length from 1,200 to 400 tokens
Used more specific instructions instead of examples
Implemented response format templates

Smart Batching:

Process similar tickets together
Cache common responses
Use smaller models for simple classifications

Circuit Breakers:

Max 3 retries per request
Daily spending limits with alerts
Automatic fallback to rule-based system if costs spike

Current monthly cost: $480 (vs. $3,200 initial)

Lesson 5: Testing AI Systems is Hard

Unit tests don't work when your system's behavior isn't deterministic.

Our Testing Approach

Golden Dataset Testing:

500 manually labeled tickets across all categories
Run full classification pipeline weekly
Alert if accuracy drops >2% from baseline

Shadow Mode:

New model versions run alongside production
Compare outputs without affecting customers
Gradual rollout only after 1 week of shadow testing

Synthetic Data Generation:

Create adversarial examples for edge cases
Generate tickets in different languages/styles
Test with intentionally confusing inputs

Concrete Example: Billing Complaint Handler

Here's how our agent handles a tricky billing complaint:

Input: "This is ridiculous! You charged me twice for Premium and your chat support is useless. I want a refund NOW or I'm canceling everything."

Agent Processing:

Sentiment analysis: Negative (confidence: 0.95)
Issue classification: Billing dispute + Double charge (confidence: 0.92)
Urgency detection: High (keywords: "ridiculous," "NOW," "canceling")
Escalation trigger: Billing + High urgency = Human review required

Output:

Routed to billing team priority queue
Human agent gets context: "Double charge complaint, high urgency, frustrated customer"
AI-suggested response saved as draft (but not sent)

Result: Human agent resolves in 2 hours instead of the previous 1-2 days.

What's Next

After six months, we're expanding cautiously:

Working on:

Multi-language support (starting with Spanish)
Integration with our knowledge base for auto-responses
Sentiment-based priority scoring

Not working on:

Fully autonomous customer calls (too risky)
Complex technical troubleshooting (requires deep domain knowledge)
Handling complaints without human oversight (learned this lesson)

Key Takeaways

Start small and constrained - Full automation from day one is a recipe for disaster
Monitoring beats testing - You can't unit test your way to reliable AI operations
Humans are force multipliers - AI + human oversight > pure automation
Cost control is operational - Budget monitoring and circuit breakers are essential
Gradual rollout works - Shadow mode and staged capabilities reduce risk

AI agents in production aren't about replacing humans—they're about making humans more effective at the work that matters most.