Why AI Agents Fail in Production (And How to Fix It)
Common pitfalls we've seen teams encounter when deploying AI agents, and battle-tested strategies to overcome them.
#The Demo-to-Production Gap
Your agent works perfectly in development. It handles every test case you throw at it. Then you deploy to production and everything breaks.
Sound familiar? You're not alone.
#Common Failure Modes
##1. Unhandled Edge Cases
Agents encounter inputs in production that never appeared in testing. Without proper guardrails, they hallucinate, loop infinitely, or produce dangerous outputs.
Solution: Implement schema validation, output filtering, and confidence thresholds.
##2. Cost Explosions
An agent that costs $0.10 per run in testing might cost $10 in production when users provide unexpected inputs that trigger expensive reasoning chains.
Solution: Set hard token limits, implement cost tracking, and use tiered model selection.
##3. Latency Spikes
Production load reveals performance bottlenecks invisible in development.
Solution: Profile tool calls, implement caching, and use async processing where possible.
#Building for Production
At Agentflare, we've baked these lessons into our platform:
- Automatic cost controls and alerts
- Built-in retry logic and circuit breakers
- Real-time performance monitoring
The result? Agents that actually work when it matters.