Who's Monitoring the Agents?
Recent advancements in frameworks like CrewAI and AutoGen have led to their deployment in production environments. However, teams are facing challenges in operating these multi-agent systems effectively, often lacking visibility into their operations. This lack of oversight can result in inefficiencies and potential data mishandling, highlighting the need for improved monitoring solutions.
- ▪Frameworks like CrewAI and AutoGen are now being used in real production settings.
- ▪Teams struggle with operational visibility, leading to inefficiencies and potential data issues.
- ▪There is a pressing need for better monitoring tools to understand how these systems function.
Opening excerpt (first ~120 words) tap to expand
Over the past few months, something quietly shifted. Frameworks like CrewAI, AutoGen, and LangGraph are no longer just showing up in demos—they’re running in production. Teams are wiring together planners, tool-using agents, retrievers, and external APIs, then handing them real work. Incident response, internal copilots, automation pipelines – it’s all starting to look less like experimentation and more like infrastructure. And once these systems are live, the problems become obvious very quickly. Not the usual “LLMs hallucinate” problem. Something more operational. Right now, we’re very good at building agents and not very good at operating them. The frameworks make composition easy, but they stop short of giving you real control once things are running at scale.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at The New Stack.
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