AgentOps: Your AI Agent Is Already Failing in Production. You Just Can’t See It
Last Updated on April 2, 2026 by Editorial Team Author(s): Divy Yadav Originally published on Towards AI. The practical guide to monitoring, debugging, and governing AI agents before they become a liability You shipped an AI agent. It worked in staging. Photo by authorFollowing the introduction, the article delves into the challenges faced by teams operating AI agents in production, emphasizing the inadequacy of traditional monitoring systems that fail to capture the nuanced failures of these agents. It introduces AgentOps as a necessary discipline for managing the lifecycle of AI agents, outlining five critical functions that enhance observability, control costs, evaluate performance, and ensure compliance in real-world applications. By sharing practical examples and potential pitfalls, t
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Last Updated on April 2, 2026 by Editorial Team
Author(s): Divy Yadav
Originally published on Towards AI.
The practical guide to monitoring, debugging, and governing AI agents before they become a liability
You shipped an AI agent. It worked in staging.
Photo by author
Following the introduction, the article delves into the challenges faced by teams operating AI agents in production, emphasizing the inadequacy of traditional monitoring systems that fail to capture the nuanced failures of these agents. It introduces AgentOps as a necessary discipline for managing the lifecycle of AI agents, outlining five critical functions that enhance observability, control costs, evaluate performance, and ensure compliance in real-world applications. By sharing practical examples and potential pitfalls, the article argues for the urgency of implementing robust observability measures before deploying AI agents to prevent costly mistakes and maintain accountability.
Read the full blog for free on Medium.
Published via Towards AI
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