AI Agents in Production: Lessons from the Trenches
What we learned deploying autonomous AI agents in enterprise environments - from hallucination management to cost optimization.
AI agents are everywhere - from customer support bots to code assistants. But deploying them in production environments where reliability, security, and cost matter is an entirely different challenge. After shipping multiple agent-based systems for enterprise clients, here is what actually works.
The Hallucination Problem
LLMs hallucinate. It is not a bug - it is a feature of how they work. The key is building guardrails that catch hallucinations before they reach users. Our approach: implement confidence scoring, fact-checking layers, and human-in-the-loop workflows for high-stakes decisions.
Cost Control at Scale
Token costs add up fast. For one client, unchecked API calls were costing $50k per month. We implemented aggressive caching, prompt compression, and strategic use of smaller models for simpler tasks. Result: 70% cost reduction with zero impact on quality.