Resolve AI says the AI coding boom is breaking production systems. It wants to fix that.

Resolve AI, a production-operations startup backed by Greylock and Lightspeed Venture Partners, today announced a sweeping expansion of its platform that introduces always-on background agents, a redesigned investigation architecture, and a shared workspace where engineers and AI agents collaborate in real time on live incidents. The centerpiece of the release is a new multi-agent investigation system developed by Resolve AI's in-house research lab. Instead of deploying a single AI agent to diagnose a production failure, the platform now dispatches a coordinated team of specialized agents that pursue multiple hypotheses in parallel, independently verify each other's conclusions, and construct complete causal chains from root cause to symptom.

The company says the architecture delivers more than a twofold improvement in root cause accuracy on its internal evaluation benchmarks compared to earlier versions of its platform. "Think of a single agent being on call, the way a human would be," Resolve AI CEO and co-founder Spiros Xanthos told VentureBeat in an exclusive interview ahead of the announcement. "We now have a team of agents that all work together, almost like a team of humans debugging an issue, and that has improved quality by 2x." The announcement arrives at a moment of acute tension in the software industry.

AI-powered code generation has exploded in adoption, enabling engineering teams to ship dramatically more software than they could two years ago. But keeping that software running in production — debugging it when it breaks, monitoring it after deployment, auditing its health — remains overwhelmingly manual. For a company that raised a $125 million Series A at a $1 billion valuation earlier this year, Resolve AI is making a direct bet that the operational side of the software lifecycle is the next major frontier for AI investment.

Resolve AI's agents now act as first responders for every on-call alert, typically triaging within five minutes before a human engineer even becomes involved. In previous public disclosures, the company has cited DoorDash reducing time to root cause by up to 87 percent. When asked to contextualize that figure, Xanthos described the typical baseline.

"When something goes wrong, it might take five to 10 minutes for a human to even get their laptop and connect," he said. "The typical MTTR is in the tens of minutes, sometimes hours, depending on severity. So an improvement of 80-plus percent — four to five times faster — is actually huge.

It's something we've never achieved before with AI, tools, data, or observability." One of the core challenges in applying large language models to high-stakes production environments is their tendency to generate plausible-sounding but incorrect answers — a failure mode that, in the context of a live outage, could send an engineering team chasing the wrong fix while a service stays down. Xanthos acknowledged this directly. "This is a very common issue with models out of the box," he said.