Paste a Slack Bug Report into an AI SRE Agent: AURA Finds the Cause

A coworker says checkout is broken and nothing else. That is the whole prompt. AURA reads the live logs and comes back with the payment service.

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Normally a message like this is the start of guessing at a service and opening dashboards until something looks wrong. Here it is the entire input: no service named, no error string, no time range.

AURA hands the question to its log analysis worker, which converts the relative time into a bounded UTC window and queries live Mezmo data from the OpenTelemetry Astronomy Shop demo. What comes back is the front-end proxy returning HTTP 500 on checkout while browse and cart return 200, traced downstream to invalid payment tokens on gold loyalty events.

It also comes back with a limit. The logs carry no shared trace or request identifier, so the analysis calls the correlation temporal and count-based rather than one-to-one trace proof, and recommends adding that correlation as a next step. Nothing in production changed.

AURA is built for on-call SREs who need a root cause from a plain-language symptom report without knowing which service to open first.

Chapters:

0:00 A coworker reports checkout failing during payment

0:10 Handing the Slack report to AURA in plain language

0:29 AURA, the Mezmo MCP server, and the OpenTelemetry demo data

0:55 The log analysis worker builds a bounded timeline

1:17 Confirming the symptom: HTTP 500 on checkout only

1:30 Tracing the failure to the payment service

1:47 Separating evidence from inference

2:08 Recommended next steps, with nothing changed in production

2:28 Pasting the analysis back into Slack

#AILogAnalysis #Observability #SRE