Title: Discussion 6 — When the Dashboard Lies (Green Spans, Failed Task)
Course: AIINFRA 300: Agentic AI & the Model Context Protocol (MCP) > Section: Week 06 — Cost, Latency, and Observability Instrumentation

💬 Discussion 6 — When the Dashboard Lies (Green Spans, Failed Task) (20 pts)

Your trace shows healthy latency and 200-status spans on every single step, but the agent still failed the task the user actually asked for. Nothing in the structural metrics — span duration, token counts, error rates — flagged a problem. In your initial post (150–250 words), answer:

- What additional signal, check, or piece of instrumentation would have caught this failure, given that structural observability (spans/latency/tokens) clearly wasn't enough on its own?
- Is this something you'd add to your tracing setup (an automated eval, a semantic check), or something that requires a human in the loop? Justify your choice.
- Give a concrete example — from this week's lab, a prior course, or a real-world agent you've read about — of a failure that would slip past structural observability but get caught by the signal you're proposing.

Then respond to at least two classmates: engage with the specific signal they proposed — would it have caught your example failure too, or does it only catch a narrower class of problems than they think?