Deductive AI Cuts DoorDash Debugging Hours by 1,000

In the era of AI‑powered code generators, software systems are growing faster than engineers can keep up with, leading to a debugging crisis that can cost companies millions in downtime. Deductive AI, a stealth startup that just raised $7.5 million in seed funding led by CRV, claims to solve this problem with “AI SRE agents” built on reinforcement learning. By mapping code, telemetry, and documentation into a dynamic knowledge graph, the agents can form, test, and converge on root‑cause hypotheses in minutes—mimicking seasoned site‑reliability engineers but at machine speed.

DoorDash’s advertising platform, which runs real‑time auctions that must finish in under 100 ms, has adopted Deductive as a core part of its incident response workflow. Over the past few months the tool has identified the root cause of roughly 100 production incidents, translating to more than 1,000 hours of engineering time saved and a revenue impact “in millions of dollars,” according to Senior Director of Engineering Shahrooz Ansari. Foursquare reported a 90 % reduction in the time required to diagnose Apache Spark job failures, cutting a process that used to take hours or days into under ten minutes and yielding over $275,000 in annual savings.

What sets Deductive apart from observability platforms like Datadog or New Relic is its code‑aware reasoning engine. The system connects to existing tools via read‑only APIs, builds and continuously updates a knowledge graph that captures service dependencies and deployment histories, and launches a multi‑agent investigation whenever an alert fires. Each agent specializes in a different data source—code changes, trace data, deployment metadata—and the agents communicate to refine hypotheses. Reinforcement learning lets the system learn from every incident which investigative steps lead to correct diagnoses, improving over time while keeping a human in the loop for validation and safety. With a pricing model based on incidents rather than data volume, Deductive positions itself as a complementary layer that can be deployed in the cloud or on‑premises, and it plans to extend its capabilities from reactive incident analysis to proactive prevention.

Key takeaway: AI-driven debugging can turn debugging from a time-consuming drain into a productivity engine, especially as AI-generated code accelerates development.