Last updated on March 4, 2026 by Editorial Team
Author(s): Divya Yadav
Originally published on Towards AI.
Why is building agents without this layer like driving blind? And how to fix it.
You know exactly where to look when traditional software malfunctions. Line numbers, stack trace and error logs. You will find the culprit in thirty seconds.
This article discusses the importance of agent observation and evaluation in the development of AI agents, emphasizing that unlike traditional software, agents present unique challenges in debugging due to their non-deterministic nature. This observation underlines the need for observational practices that enable developers to understand discrepancies between an agent’s actual actions and expected behaviors, and highlights the contradiction between traditional software testing and evaluation of agents, emphasizing that a new framework is necessary to address agent failures and ensure reliability in production environments. The article also presents various evaluation techniques for agents, such as single-step and multi-turn evaluation, providing information on how these methods can be effectively implemented.
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Published via Towards AI
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Comment: The content of the article represents the views of the contributing authors and not those of AI.
