Traversal raises $120 million at a $1 billion valuation for AI agents that investigate software outages
New York startup Traversal has raised a $120 million Series B at a $1 billion valuation, co-led by Kleiner Perkins, Redpoint and Khosla. Its AI agents investigate production incidents. A benchmark it built with academic partners shows frontier agents still solve only about a quarter of medium-difficulty incidents.
Traversal announced a $120 million Series B on 3 November, valuing the company at $1 billion. Kleiner Perkins, Redpoint Ventures and Khosla Ventures co-led, with Sequoia Capital, Battery Ventures and Amex Ventures participating. Total funding is now $173 million.
The company builds AI agents for site reliability engineering. Its platform maps telemetry, code and deployment history, searches many possible causes at once, starts investigations when alerts fire, and shares findings with coding agents through MCP.
Traversal says customers include Nubank, Harvey, DigitalOcean, PepsiCo and American Express. It reports annual recurring revenue up ninefold this year and monthly investigations rising from 513 to nearly 73,000, though it declined to disclose revenue figures, so the growth claim is unverified.
The company also published ORCA-bench with Cornell Tech and Columbia: 1,079 tasks on which the best of five frontier agents correctly found root causes for only 25.3 per cent of medium-difficulty incidents. Accuracy fell to 10 per cent with vague reports, and hallucination rates reached 40 per cent.
Because Traversal helped build the benchmark, it reflects the company's own framing of the problem, which is that AI-written code is breaking systems in new ways.
Why it matters
AI coding agents are shipping more software, and the burden is shifting to operations. Funding for AI site reliability tools signals where enterprise budgets are heading, and the benchmark is a useful reminder of current limits.
Indicops view
Indian IT-services firms and platform teams running large estates should pilot AI incident tools for triage and release-risk checks first, with humans owning remediation. Measure root-cause accuracy on your own incidents before trusting autonomous fixes.
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