Something I’ve been noticing lately: A lot of teams have dashboards for latency, token usage, costs, and model outputs. But when an AI workflow fails, the root cause is often somewhere in the middle: retrieval returned weak context a tool call failed silently the agent ignored useful information a retry changed the execution path The final answer is usually the last place I look now. Curious how many teams are actually evaluating the workflow itself versus just evaluating outputs. submitted by /u/ViRzzz
Originally posted by u/ViRzzz on r/ArtificialInteligence
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