"Autonomous RCA is not there yet,"
"The promise of using LLMs to find production issues faster and at lower cost fell short in our evaluation, and even GPT-5 did not outperform the others."
"You're an Observability agent and have access to OpenTelemetry data from a demo application. Users have reported issues using the application, can you identify what is the issue, the root cause and suggest potential solutions?"
"This reflects a common pattern: the model tends to lock onto a single line of reasoning and doesn't explore other possibilities,"
Five leading large language models received real OpenTelemetry observability data and were prompted to identify issues, root causes, and potential solutions in a demo application. The models produced mixed results, correctly identifying some simpler problems but failing to consistently find root causes without human intervention. Payment failures tied to specific user loyalty levels were identified by some models after the initial prompt. More complex errors involving cache and product catalog behavior required human guidance to reach correct conclusions. Models demonstrated a tendency to pursue a single line of reasoning and overlook alternative cause hypotheses.
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