How Deductive AI saved DoorDash 1,000 engineering hours by automating software debugging
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 Wednesday, 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 documentation. When an incident occurs, multiple AI agents work together to form hypotheses, test them against live system evidence, and converge on a root cause — mimicking the investigative workflow of experienced site reliability engineers, but completing the process in minutes rather than hours.
The technology has already shown measurable impact at some of the world's most demanding production environments. DoorDash's advertising platform, which runs real-time auctions that must complete in under 100 milliseconds, has integrated Deductive into its incident response workflow. The company has set an ambitious 2026 goal of resolving production incidents within 10 minutes.
"Our Ads Platform operates at a pace where manual, slow-moving investigations are no longer viable. Every minute of downtime directly affects company revenue," said Shahrooz Ansari, Senior Director of Engineering at DoorDash, in an interview with VentureBeat. "Deductive has become a critical extension of our team, rapidly synthesizing signals across dozens of services and surfacing the insights that matter—within minutes."
DoorDash estimates that Deductive has root-caused approximately 100 production incidents over the past few months, translating to more than 1,000 hours of annual engineering productivity and a revenue impact "in millions of dollars," according to Ansari. At location intelligence company Foursquare, Deductive reduced the time to diagnose Apache Spark job failures by 90% —t urning a process that previously took hours or days into one that completes in under 10 minutes — while generating over $275,000 in annual savings.
Why AI-generated code is creating a debugging crisis
The timing of Deductive's launch reflects a brewing tension in software development: AI coding assistants are enabling engineers to generate code faster than ever, but the resulting software is often harder to understand and maintain.
"Vibe coding," a term popularized by AI researcher Andrej Karpathy, refers to using natural-language prompts to generate code through AI assistants. While these tools accelerate development, they can introduce what Agarwal describes as "redundancies, breaks in architectural boundaries, assumptions, or ignored design patterns" that accumulate over time.
"Most AI-generated code still introduces redundancies, breaks architectural boundaries, makes assumptions, or ignores established design patterns," Agarwal told Venturebeat. "In many ways, we now need AI to help clean up the mess that AI itself is creating."
The claim that engineers spend roughly half their time on debugging isn't hyperbole. The Association for Computing Machinery reports that developers spend 35% to 50% of their time validating and debugging software. More recently, Harness's State of Software Delivery 2025 report found that 67% of developers are spending more time debugging AI-generated code.
"We've seen world-class engineers spending half of their time debugging instead of building," said Rakesh Kothari, Deductive's co-founder and CEO. "And as vibe coding generates new code at a rate we've never seen, this problem is only going to get worse."
How Deductive's AI agents actually investigate production failures
Deductive's technical approach differs substantially from the AI features being added to existing observability platforms like Datadog or New Relic. Most of those systems use large language models to summarize data or identify correlations, but they lack what Agarwal calls "code-aware reasoning"—the ability to understand not just that something broke, but why the code behaves the way it does.
"Most enterprises use multiple observability tools across different teams and services, so no vendor has a single holistic view of how their systems behave, fail, and recover—nor are they able to pair that with an understanding of the code that defines system behavior," Agarwal explained. "These are key ingredients to resolving software incidents and it is exactly the gap Deductive fills."
The system connects to existing infrastructure using read-only API access to observability platforms, code repositories, incident management tools, and chat systems. It then continuously builds and updates its knowledge graph, mapping dependencies between services and tracking deployment histories.
When an alert fires, Deductive launches what the company describes as a multi-agent investigation. Different agents specialize in different aspects of the problem: one might analyze recent code changes, another examines trace data, while a third correlates the timing of the incident with recent deployments. The agents share findings and iteratively refine their hypotheses.
The critical difference from rule-based automation is Deductive's use of reinforcement learning. The system learns from every incident which investigative steps led to correct diagnoses and which were dead ends. When engineers provide feedback, the system incorporates that signal into its learning model.
"Each time it observes an investigation, it learns which steps, data sources, and decisions led to the right outcome," Agarwal said. "It learns how to think through problems, not just point them out."
At DoorDash, a recent latency spike in an API initially appeared to be an isolated service issue. Deductive's investigation revealed that the root cause was actually timeout errors from a downstream machine learning platform undergoing a deployment. The system connected these dots by analyzing log volumes, traces, and deployment metadata across multiple services.
"Without Deductive, our team would have had to manually correlate the latency spike across all logs, traces, and deployment histories," Ansari said. "Deductive was able to explain not just what changed, but how and why it impacted production behavior."
The company keeps humans in the loop—for now
While Deductive's technology could theoretically push fixes directly to production systems, the company has deliberately chosen to keep humans in the loop—at least for now.
"While our system is capable of deeper automation and could push fixes to production, currently, we recommend precise fixes and mitigations that engineers can review, validate, and apply," Agarwal said. "We believe maintaining a human in the loop is essential for trust, transparency and operational safety."
However, he acknowledged that "over time, we do think that deeper automation will come and how humans operate in the loop will evolve."
Databricks and ThoughtSpot veterans bet on reasoning over observability
The founding team brings deep expertise from building some of Silicon Valley's most successful data infrastructure platforms. Agarwal earned his Ph.D. at UC Berkeley, where he created BlinkDB, an influential system for approximate query processing. He was among the first engineers at Databricks, where he helped build Apache Spark. Kothari was an early engineer at ThoughtSpot, where he led teams focused on distributed query processing and large-scale system optimization.
The investor syndicate reflects both the technical credibility and market opportunity. Beyond CRV's Max Gazor, the round included participation from Ion Stoica, founder of Databricks and Anyscale; Ajeet Singh, founder of Nutanix and ThoughtSpot; and Ben Sigelman, founder of Lightstep.
Rather than competing with platforms like Datadog or PagerDuty, Deductive positions itself as a complementary layer that sits on top of existing tools. The pricing model reflects this: Instead of charging based on data volume, Deductive charges based on the number of incidents investigated, plus a base platform fee.
The company offers both cloud-hosted and self-hosted deployment options and emphasizes that it doesn't store customer data on its servers or use it to train models for other customers — a critical assurance given the proprietary nature of both code and production system behavior.
With fresh capital and early customer traction at companies like DoorDash, Foursquare, and Kumo AI, Deductive plans to expand its team and deepen the system's reasoning capabilities from reactive incident analysis to proactive prevention. The near-term vision: helping teams predict problems before they occur.
DoorDash's Ansari offers a pragmatic endorsement of where the technology stands today: "Investigations that were previously manual and time-consuming are now automated, allowing engineers to shift their energy toward prevention, business impact, and innovation."
In an industry where every second of downtime translates to lost revenue, that shift from firefighting to building increasingly looks less like a luxury and more like table stakes.
