As software systems grow more complex and AI tools generate code faster than ever, a fundamental problem is getting worse: Engineers are drowning in debugging work, spending up to half their time hunting down the causes of software failures instead of building new products. The challenge has become so acute that it’s creating a new category of tooling — AI agents that can diagnose production failures in minutes instead of hours.Deductive AI, a startup emerging from stealth mode Tuesday, believes it has found a solution by applying reinforcement learning — the same technology that powers game-playing AI systems — to the messy, high-stakes world of production software incidents. The company announced it has raised $7.5 million in seed funding led by CRV, with participation from Databricks Ventures, Thomvest Ventures, and PrimeSet, to commercialize what it calls “AI SRE agents” that can diagnose and help fix software failures at machine speed.The pitch resonates with a growing frustration inside engineering organizations: Modern observability tools can show that something broke, but they rarely explain why. When a production system fails at 3 a.m., engineers still face hours of manual detective work, cross-referencing logs, metrics, deployment histories, and code changes across dozens of interconnected services to identify the root cause.”The complexities and inter-dependencies of modern infrastructure means that investigating the root cause of an outage or incident can feel like searching for a needle in a haystack, except the haystack is the size of a football field, it’s made of a million other needles, it’s constantly reshuffling itself, and is on fire — and every second you don’t find it equals lost revenue,” said Sameer Agarwal, Deductive’s co-founder and chief technology officer, in an exclusive interview with VentureBeat.Deductive’s system builds what the company calls a “knowledge graph” that maps relationships across codebases, telemetry data, engineering discussions, and internal documentat …
