What We Learned Running AI Agents in Production for Six Months
Real lessons from deploying autonomous AI systems: the unexpected failure modes, monitoring challenges, and practical patterns that actually work in production environments.
What We 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 agent analyzes incoming tickets, categorizes them, assigns priority levels, and routes them to appropriate teams. Here's what we learned from 50,000+ processed tickets.
The Good: Where Autonomy Actually Works
Pattern Recognition Exceeds Expectations
Our AI agent correctly categorizes 94% of tickets compared to our previous rule-based system's 78% accuracy. It learned to identify edge cases we never programmed for:
- Customers describing technical issues using business terminology
- Multi-issue tickets that need splitting
- Urgent requests buried in casual language
Consistent Decision Making
Unlike human operators, the agent doesn't have bad days or inconsistent judgment calls. Priority assignments remain stable regardless of time of day or ticket volume.
The Challenging: Unexpected Failure Modes
Context Window Limitations Hit Hard
Our biggest surprise: the agent would "forget" important context from earlier in long email threads. We implemented a context summarization step that extracts key facts and preserves them across interactions.
Solution: Maintain a structured context summary that gets updated with each interaction:
Customer: Enterprise client (Tier 1)
Issue type: API authentication
Previous attempts: Reset API key twice
Escalation history: None
Urgency indicators: Production system down
The "Confident Wrong Answer" Problem
The agent would sometimes make decisive classifications with high confidence while being completely wrong. A customer asking about "server migration" got routed to our marketing team because the agent associated "migration" with "customer onboarding."
Solution: We added uncertainty detection. When the agent's confidence score falls below 85%, it flags the ticket for human review instead of making an autonomous decision.
Drift Over Time
We noticed classification accuracy slowly declining after month three. The agent was learning from its own outputs in a feedback loop, gradually shifting away from our intended behavior.
Solution: Monthly retraining with curated datasets and regular A/B testing against baseline performance.
Monitoring: What Actually Matters
Traditional Metrics Miss the Point
We started tracking typical ML metrics like accuracy and F1 scores. These didn't correlate with business impact.
Better metrics we discovered:
- Time to first human touchpoint
- Escalation rate by category
- Customer satisfaction with initial routing
- False positive rate for urgent classifications
Human-in-the-Loop Feedback Quality
Not all human feedback is equally valuable. We learned to weight feedback from domain experts more heavily and ignore corrections made under time pressure.
Practical Architecture Patterns
Circuit Breakers for AI Systems
We implemented circuit breakers that automatically fall back to rule-based systems when:
- AI confidence drops below threshold for >10 consecutive decisions
- Response time exceeds 30 seconds
- Error rate spikes above 5%
Async Processing with Human Checkpoints
For non-urgent tickets, we introduced a 15-minute delay before final routing. This gives human operators a window to catch and correct obvious mistakes without slowing down the system.
Version Control for Agent Behavior
We treat agent configurations like code:
- All prompt changes go through review
- A/B test new versions against current production
- Rollback capability within 5 minutes
- Clear documentation of what each version optimizes for
The Surprising Social Dynamics
Team Resistance Came from Unexpected Places
Our support managers embraced the system quickly—they loved the consistent workload distribution. Resistance came from senior support agents who felt their expertise in ticket triage was being devalued.
Solution: We repositioned the AI as handling "easy" cases so experts could focus on complex problems. Usage data backed this up: average ticket complexity handled by humans increased 40%.
Customer Awareness Matters
Customers who knew an AI was involved in routing were more patient with initial categorization mistakes. Those who didn't know often escalated immediately when routing seemed wrong.
What We'd Do Differently
Start with Narrow Scope
We initially tried to automate the entire routing workflow. Starting with just priority detection would have been smarter—simpler to validate and debug.
Invest in Observability Earlier
Our logging was built for debugging individual requests, not understanding system-wide patterns. We rebuilt our observability stack in month four to track decision flows across multiple interactions.
Plan for Model Updates from Day One
Updating the model required significant engineering work because we didn't architect for it initially. Design your system assuming you'll need to swap models frequently.
The Bottom Line
Autonomous AI operations work best for well-defined, repeatable tasks with clear success metrics. The technology handles the mechanics well, but the operational challenges—monitoring, feedback loops, and human integration—are harder than expected.
Our agent now processes 85% of incoming tickets autonomously, reducing average routing time from 2 hours to 3 minutes. The remaining 15% that need human review are genuinely complex cases that benefit from expert attention.
The key insight: successful autonomous AI isn't about replacing human judgment—it's about creating systems that know when to ask for help.