Labs Are Starving for Real-World Agent Failure Data, Not More Synthetic Text
heyshrutimishra · x · 2026-10-15
The author argues labs drowning in synthetic text, code, and math are starving for the one data type that matters: messy real-world agent failure logs.
- Example: an invoice-cleaning agent calls the wrong API with valid credentials, scrambles 400 rows, logs 'task complete,' and nobody notices for two weeks
- A single trace of how an agent breaks and gets caught is worth more than a million pristine training examples
- Nobody publishes this data — too messy, embarrassing, and legally risky — so it stays locked in enterprise servers; it must be earned deployment by deployment
- Whoever captures failure data owns the eval layer, and whoever owns the eval layer controls what every lab optimizes next. That's the real moat
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