What We Learned Running AI Agents in Production for 6 Months

Six months ago, we deployed our first autonomous AI agent to handle customer support ticket routing. The agent reads incoming tickets, categorizes them, and assigns them to the right team. It sounds straightforward—until you run it in production.

Here's what we learned about autonomous AI operations, including the mistakes that taught us the most.

The Fundamentals That Actually Matter

Start with Observable Outputs, Not Smart Inputs

Our first instinct was to make the AI smarter—better prompts, more context, fancier models. Wrong focus.

The breakthrough came when we shifted to making outputs observable:

• Confidence scores for every decision
• Reasoning traces showing why each choice was made
• Fallback triggers when confidence drops below thresholds
• Human handoff points clearly defined

Example: Instead of trying to teach the agent about every edge case in ticket routing, we built a confidence threshold. When confidence drops below 0.7, tickets go to a human reviewer. Simple, but it caught 89% of misrouting errors.

Design for Graceful Degradation

Autonomous doesn't mean unmonitored. Build multiple safety nets:

1. Circuit breakers: Stop processing when error rates spike
2. Rate limiting: Prevent runaway operations
3. Rollback mechanisms: Quick return to previous working state
4. Manual override: Always available, clearly documented

A Concrete Example: The 3 AM Incident

At 3:17 AM on a Tuesday, our agent started miscategorizing urgent security tickets as "general inquiries." Here's how it unfolded:

What happened: A new type of phishing email triggered edge cases in our categorization logic. The agent was confident in its (wrong) decisions.

What we caught quickly: Our monitoring detected unusual routing patterns within 12 minutes. Confidence scores were normal, but distribution patterns were off.

What we missed: The agent's reasoning was internally consistent but based on incomplete training data. It confidently made wrong decisions.

The fix: We implemented cross-validation checks. Before final routing, the agent now runs a secondary "sanity check" model trained specifically on edge cases. If the two models disagree significantly, human review is triggered.

Operational Patterns That Work

Monitor Behavior, Not Just Performance

Traditional metrics (accuracy, response time) don't tell the full story. Track behavioral patterns:

• Decision distribution: Are certain categories suddenly over/under-represented?
• Confidence patterns: Sudden shifts in average confidence scores
• Reasoning consistency: Are explanations coherent and stable?
• Edge case frequency: How often are fallbacks triggered?

Build Feedback Loops Early

Your AI agent will make mistakes. The question is how quickly you catch and correct them:

1. Real-time validation: Immediate checks on high-stakes decisions
2. Daily audits: Sample review of recent decisions
3. Weekly analysis: Pattern detection and model drift assessment
4. Monthly retraining: Incorporate new data and edge cases

Document Everything, Especially Failures

Keep detailed logs of:

• Decision rationale
• Input data characteristics
• Model versions and configurations
• Failure modes and recovery actions

This documentation becomes training data for the next iteration.

What Breaks (And When)

Data Drift Happens Faster Than Expected

Our ticket routing agent started showing decreased accuracy after just 3 weeks. Customer language patterns had shifted slightly, but enough to impact performance.

Solution: Continuous monitoring of input data distributions. Weekly automated reports flag when new patterns emerge.

Edge Cases Compound

Rare events (1% frequency) become common when processing thousands of items daily. One "rare" edge case per 100 tickets means 20+ edge cases per day at our volume.

Solution: Design for edge cases from day one. Budget 30% of development time for edge case handling.

Human-AI Handoffs Are the Hardest Part

The transition points between autonomous operation and human intervention need the most attention. Unclear handoffs create confusion and delays.

Solution: Explicit handoff protocols. When handing off to humans, provide:

• Clear context about what the AI tried
• Confidence scores and reasoning
• Specific questions for human decision-maker
• Escalation path if human is also uncertain

Practical Implementation Steps

1. Start small: Pick one well-defined process with clear success metrics
2. Build observability first: Instrument everything before optimizing anything
3. Plan for failure: Design error handling before error prevention
4. Test edge cases: Systematically explore boundary conditions
5. Train your team: Human operators need to understand AI decision-making

The Bottom Line

Autonomous AI operations work, but they require different thinking than traditional automation. Focus on observability, plan for graceful failures, and remember that "autonomous" doesn't mean "unsupervised."

The goal isn't to remove humans from the loop entirely—it's to make human oversight more effective and targeted. Six months in, our agent handles 78% of tickets autonomously, with human intervention focused on genuinely complex cases.

That's not just efficiency—it's better outcomes for everyone.