How Deductive AI saved DoorDash engineering hours

2 hours ago7 min read

As software architectures grow increasingly Byzantine and AI-assisted coding accelerates development velocity, a paradoxical crisis is emerging: engineering organizations are becoming mired in diagnostic work, with professionals reportedly spending 35-50% of their cycles hunting software failures rather than building new capabilities. This debugging bottleneck has intensified precisely as AI coding assistants enable unprecedented code generation speeds, creating what Deductive AI co-founder Sameer Agarwal describes as a situation where 'we now need AI to help clean up the mess that AI itself is creating.' The startup, emerging from stealth with $7. 5 million in seed funding led by CRV with participation from Databricks Ventures, addresses this through what might be considered computational epistemology—applying reinforcement learning, the technology underpinning sophisticated game-playing systems, to the chaotic domain of production software incidents.Their approach diverges fundamentally from the correlation-based alerts of conventional observability platforms; instead, Deductive constructs a dynamic knowledge graph mapping relationships across codebases, telemetry, and documentation, then deploys multiple specialized AI agents that collaboratively form and test hypotheses against live system evidence. This methodology has demonstrated remarkable efficacy at DoorDash's advertising platform, where real-time auctions must complete within 100 milliseconds and downtime directly impacts revenue.According to Senior Director of Engineering Shahrooz Ansari, Deductive has root-caused approximately 100 production incidents over recent months, reclaiming over 1,000 engineering hours annually with revenue impact measured in millions. Similarly, at Foursquare, diagnosis time for Apache Spark job failures decreased by 90%, transforming a process that previously required hours or days into one completing in under 10 minutes while generating $275,000 in annual savings.The technical foundation is noteworthy: rather than merely summarizing data like LLM-enhanced observability tools, Deductive's system employs what Agarwal terms 'code-aware reasoning'—understanding not just that something broke, but why the code behaves as it does. Through read-only API integrations with existing infrastructure, the platform continuously builds its knowledge graph, and when alerts trigger, specialized agents analyze code changes, trace data, and deployment timelines in a coordinated investigation that learns from each incident via reinforcement learning.This represents a significant evolution beyond rule-based automation, as the system internalizes which investigative pathways yield correct diagnoses versus dead ends. The founding team's pedigree—Agarwal created BlinkDB at UC Berkeley and was among Databricks' first engineers, while CEO Rakesh Kothari led distributed systems at ThoughtSpot—lends credibility to their ambitious vision. Currently maintaining human oversight for operational safety, the technology nonetheless points toward a future where AI systems not only identify software failures but potentially prevent them proactively, shifting engineering effort from reactive firefighting to strategic innovation in an era where every second of downtime carries tangible business consequence.

#Deductive AI

#software debugging

#AI agents

#engineering productivity

#DoorDash

#featured

#reinforcement learning

#root cause analysis

#production incidents

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