In 2025, a quiet revolution is reshaping software development: AI is now writing more code than humans. Tools like GitHub, Copilot, Cursor, and Windsor have turbocharged productivity, enabling developers to ship features at unprecedented speed. But with this explosion of AI-generated code comes a hidden crisis—one that’s already costing enterprises $400 billion annually in downtime.
When complex software systems fail—and they will fail—nobody fully understands why. Teams scramble across Slack channels, pointing fingers, escalating incidents, and burning precious minutes (or hours) just to identify the root cause. In hospitals, banks, airlines, and law enforcement agencies, these outages aren’t just inconvenient—they’re catastrophic.
Enter AI Site Reliability Engineers (AI SREs): the next frontier in software resilience. Companies like Traversal, backed by Sequoia Capital and Kleiner Perkins with a $48 million Series A, are pioneering AI systems that don’t just write code—but autonomously debug, diagnose, and even fix it when things go wrong.
This isn’t science fiction. It’s happening now. And it may be the only way we survive the coming Cambrian explosion of AI-written software.
The Hidden Cost of AI-Generated Code
For years, developers dreamed of AI assistants that could turn natural language into functional code. Today, that dream is reality—but with unintended consequences.
“So much more code is being written now by Cursor or Windsurf or Copilot… No one really understands all of it.”
This quote from Anish Agarwal, CEO and co-founder of Traversal, captures a growing industry-wide anxiety. When humans write code, they carry context: why a function exists, how modules interact, what edge cases were considered. But AI-generated code often lacks this semantic awareness. It’s syntactically correct—but contextually opaque.
The result? Software systems are becoming black boxes, even to their own creators.
The $400 Billion Downtime Problem
According to Gartner and IDC, enterprise downtime costs $5,600 per minute on average—scaling to $300,000–$1 million per hour for large organizations. Multiply that across global outages affecting airlines, banks, and healthcare systems, and you reach the staggering $400 billion annual figure.
Traditional incident response is painfully slow:
- Engineers ping-pong between teams (“It’s not my service!”)
- War rooms balloon from 5 to 80 people
- Root cause analysis takes hours—sometimes days
In a world where every second of downtime equals lost revenue, trust, and safety, this model is unsustainable.
Introducing the AI Site Reliability Engineer
Traversal isn’t building another monitoring dashboard. They’re creating an autonomous AI agent that acts as a 24/7 Site Reliability Engineer—one that:
- Ingests logs, metrics, traces, configs, and code
- Uses causal machine learning to distinguish correlation from causation
- Generates evidence-backed root cause hypotheses (like Perplexity.ai, but for your observability stack)
- Recommends or even executes fixes automatically
Think of it as Sherlock Holmes meets DevOps—an AI detective that sifts through terabytes of noise to find the one smoking gun.
Real-World Impact: 37% Faster Incident Resolution
In a six-month pilot with DigitalOcean—a top-three cloud provider serving over 600,000 developers—Traversal reduced mean time to resolution (MTTR) by 37%.
For DigitalOcean, whose infrastructure powers countless startups and SMBs, this isn’t just efficiency—it’s customer retention and brand trust. When their platform stutters, thousands of businesses feel it instantly.
“Every minute of downtime is worth thousands—or millions—of dollars,” says Athalye.
Why This Isn’t Just Another AI Hype Play
Many AI startups promise automation but deliver brittle prototypes. Traversal’s journey reveals why this problem is deceptively hard—and why few will succeed.
From 90% Accuracy to 0%—and Back Again
Their first MVP worked brilliantly on small companies: 90% diagnostic accuracy. But when tested on enterprise-scale systems like DigitalOcean? Accuracy dropped to 0%.
Why? Because real-world software isn’t neat. It’s:
- Multi-cloud, multi-tenant, and massively distributed
- Laced with legacy code, undocumented APIs, and configuration drift
- Generating petabytes of noisy, unstructured observability data
The breakthrough came when the team stopped trying to replicate human intuition and instead leaned into what AI does best: massive-scale inference and pattern recognition.
“Reasoning models are very good at detective stories… You have a mystery novel and you’re trying to figure out who did the crime.”
By framing incident response as a causal reasoning puzzle, Traversal unlocked the true potential of modern LLMs—not as coders, but as investigators.
The Bigger Vision: Reinventing Software Maintenance
Beyond fixing outages, Traversal is tackling a philosophical shift in engineering culture.
“Over time, all engineers will be doing will be troubleshooting… That would be sad.”
Anish argues that AI should free engineers to do what they love: creative system design, architecture, and innovation—not endless debugging.
But today, the opposite is happening:
- Developers spend 30–50% of their time on maintenance (Stripe, 2023)
- AI-generated code increases technical debt due to lack of context
- Junior engineers drown in alert fatigue, unable to distinguish signal from noise
The solution? Autonomous maintenance systems that:
- Continuously validate AI-written code
- Self-heal common failure patterns
- Escalate only the truly novel issues to humans
This isn’t about replacing engineers—it’s about elevating them.
The Science Behind the AI SRE: Causal ML + Reinforcement Learning
Traversal’s secret sauce lies in its founders’ deep research in causal machine learning—a field focused on understanding cause-and-effect, not just correlations.
Why Causality Matters in Debugging
Traditional ML might notice:
“When CPU spikes, errors increase.”
But that’s correlation. Causality asks:
“Did the CPU spike cause the errors—or did a memory leak cause both?”
This distinction is critical during outages. False leads waste hours. Traversal’s AI uses:
- Counterfactual reasoning: “What if this service hadn’t failed?”
- Intervention modeling: Simulating fixes before applying them
- Temporal logic: Understanding event sequences across microservices
Combined with reinforcement learning, the system learns from every incident—improving its diagnostic accuracy over time.
The Human Element: Grit, Team, and Vision
Behind the tech is a human story. Anish Agarwal—a former MIT researcher inspired by AlphaGo’s self-play creativity—left academia because he sensed a “once-in-a-lifetime” shift with generative AI.
“It just felt like a religious experience… the world is fundamentally changed.”
But building in uncertainty is hard. Startups fail. Models underperform. Investors doubt.
His advice?
“Surround yourself with people you care about, who you want to be like… You live and die by the people you surround yourself with.”
From PhD advisors to Sequoia partners to early customers like DigitalOcean, the right team turns impossible problems into breakthroughs.
What’s Next? The Industrial Age of AI
We’ve entered what Anish calls the “industrial age of artificial intelligence.” Just as factories automated physical labor in the 19th century, AI is now automating cognitive labor—especially in software.
But unlike factory machines, AI systems are adaptive, contextual, and collaborative. The winners won’t be those who merely apply AI to old workflows—but those who reinvent entire paradigms.
For software engineering, that means:
- Shift-left observability: Bake diagnostics into the development lifecycle
- Self-healing architectures: Systems that detect, diagnose, and recover autonomously
- AI co-pilots for SREs: Not replacing humans, but amplifying their impact
Final Thoughts: Embracing the Unknown
Building an AI SRE isn’t easy. The problem is simple to state—“fix broken software fast”—but fiendishly complex to solve. Yet that’s where the opportunity lies.
“If it was easy, everyone could do it.”
As AI writes more code, the need for intelligent maintenance will only grow. Companies that ignore this trend risk catastrophic outages and eroding trust. Those who embrace AI SREs will gain resilience, speed, and engineering leverage.
The future of software isn’t just about writing code faster.
It’s about understanding it deeply—even when no human can.
And in that future, the AI Site Reliability Engineer won’t be a luxury.
It’ll be mission-critical.
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