6개월간 AI 에이전트를 운영하며 느낀 점: 프레임워크는 사실 별로 중요하지 않다.
After 6 months of running AI agents in production I think the framework you pick barely matters. The thing that kills them is something else.
핵심 요약
AI 에이전트의 성패는 프레임워크 선택보다 메모리, 루프 탐지, 관측 가능성 같은 인프라 설계에 달려 있다.
- 프레임워크 무용론 — LangChain이나 CrewAI 같은 도구보다 실제 운영 환경의 안정성이 훨씬 중요함.
- 운영의 핵심 요소 — 메모리 지속성, 루프 탐지, 감사 추적(audit trail)이 에이전트의 생존을 결정함.
- 비용 관리 문제 — 에이전트가 무한 루프에 빠져 예산을 탕진하는 상황을 방지하는 설계가 필수적임.
- 공유 메모리 필요성 — 여러 에이전트가 협업할 때 일관된 정보를 유지하기 위한 공유 메모리 계층이 반드시 필요함.
Going to get downvoted for this but here we go. I've been running about 30 agents in production for paying customers for the last 6 months and I'm convinced the framework debate is mostly a distraction.
LangChain, CrewAI, AutoGen, OpenAI Agents SDK. Pick whichever one your team already knows. It doesn't matter as much as you think.
What actually decides whether your agent works in production is something almost nobody talks about on this sub, and it isn't in the framework.
Here's what I've seen kill more agents than every framework bug combined.
The agent gets stuck in a loop. It calls the same tool 200 times in 4 minutes because something downstream returned ambiguous data and the LLM decided to retry forever. Your OpenAI bill goes from $3 a day to $400 in one afternoon. By the time you notice you've burned a grand. You can't even tell which agent did it because there's no audit trail.
Your VPS reboots overnight for kernel patches. Every agent that was mid-task loses everything. Tomorrow morning the support agent has no memory of yesterday's tickets, the research crew has forgotten what they were investigating, the pipeline agent restarts from scratch. None of these are framework problems. They're memory and state problems.
A customer complains the agent gave them wrong info three days ago. You go to debug. There's no record of what the agent saw, what it decided, or which tool calls it made. The framework didn't log that because frameworks aren't observability tools. You shrug and refund.
You scaled to 15 agents working together. Two of them have conflicting beliefs about the same customer because their memory isn't shared. The customer gets two different answers in the same conversation depending on which agent replies first.
You've been around enough times to realize the part you actually need isn't in the framework at all.
What I think the real stack is.
The framework just orchestrates LLM calls. Use whatever your team likes. It's the cheap layer.
A persistent memory layer that survives crashes, restarts, and redeploys, so the agent has actual continuity. This is the layer that decides whether your agent is a toy or a product.
Loop detection at the runtime layer, not bolted on as a wrapper around the framework. Something that catches your agent making the same call too many times in a row and stops it before the bill explodes.
