Monitoring Can't Keep Up: What to Auto-Detect When Your AI System Scales
goyalshaliniuk · x · 2026-10-03
At small scale developers can manually inspect AI outputs; at large scale that's impossible, argues Shalini Goyal in a thread on scaling failures.
She lists what automated monitoring must detect: quality drops, latency spikes, cost increases, tool failures, hallucinations and error patterns. Fix: build observability and evaluation into the system from day one.
Related event: Why AI Systems Need Automated Monitoring and Security at Scale(2 posts)→
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