On July 19, 2024, a single flawed software update brought much of the digital world to a stop. Screens froze in hospitals, airports, and banks as roughly 8.5 million computers crashed at once, and the cleanup fell to engineers who had to fix many of the machines by hand. The incident, traced to a faulty update from the cybersecurity vendor CrowdStrike, cost Fortune 500 companies an estimated $5.4 billion, and about one in four of those companies was disrupted. It was an extreme version of an everyday truth: modern life runs on software so interconnected that when something breaks, working out what broke can be harder than repairing it. One widely cited study puts the cost of unplanned downtime at roughly $9,000 a minute for large organizations.
Anil Reddy Putlacheru has built his career on that second problem: not just repairing systems, but teaching them to explain their own failures. A senior software engineer who specializes in AI-powered systems integration and a Senior Member of the IEEE, a grade held by only about a tenth of the organization’s more than 400,000 members, he has spent close to two decades moving between hardware reliability, cloud infrastructure, and the machine learning systems that now sit on top of both. His focus today is intelligent diagnostics: software that watches a sprawling system, notices when something is wrong, and figures out why in seconds rather than hours.
The Needle in a Million Logs
The scale of the problem has outgrown the way most teams still work. In a February 2026 survey of more than 1,000 reliability and operations professionals, the majority reported spending 40 percent or more of their time managing incidents rather than building new products, and most said a serious incident pulls in three or more engineers at once. The deeper issue is noise. One analysis found teams receiving more than 2,000 alerts a week, of which only about 3 percent required any human action. Engineers, in other words, spend their days hunting for a few real signals buried in a flood of false ones.
This is the problem Reddy set out to automate. He built an AI-driven diagnostic agent that automatically identifies the most likely source of a failure in a complex, distributed system. Instead of asking an engineer to trace an error by hand across dozens of applications, the agent correlates telemetry, log patterns, and service dependencies between services to point directly at where a problem began. The effect is striking: incident triage that once took several hours now finishes in under 30 seconds, saving an estimated four to six hours per engineer each day, depending on how many incidents arrive.
“The hardest part of fixing a modern system usually isn’t the fix,” Reddy says. “It’s finding out what actually broke. Once you can localize the failure in seconds instead of hours, everything downstream gets calmer. Engineers stop firefighting and start building again.”
When Debugging Becomes a Conversation
The industry has taken notice. Spending on AIOps, the use of artificial intelligence to run IT operations, is projected to grow from about $12 billion in 2024 to more than $120 billion by 2034, as companies race to automate root-cause analysis and repair. The newest wave goes beyond dashboards and alerts, using large language models to let engineers simply ask their systems what is going on. In the same 2026 reliability survey, nearly three-quarters of executives said their organizations were already turning to AI to tackle these problems.
Reddy’s most ambitious project points squarely at that future. As principal architect and primary developer, he built an enterprise engineering-intelligence platform on top of large language models and a multi-agent design, combining semantic search, vector embeddings, retrieval-augmented generation, and the emerging Model Context Protocol so that AI agents could interact directly with engineering systems. The platform folded fragmented knowledge and troubleshooting steps into a single conversational interface, letting engineers investigate incidents, analyze logs, generate database queries, and diagnose cloud and container problems just by asking. Adopted across an entire quality-engineering organization and used by more than 50 engineers, it was the organization’s first engineering-intelligence platform, and it cut investigations that once took hours down to minutes.
Reddy is wary of the industry’s habit of building AI that simply produces more code or more tests. “Nobody needs an AI that writes tests for the sake of writing tests,” he explains. “What organizations actually need is an AI that understands their systems well enough to tell them what is wrong and why. That is a much harder, and much more valuable, problem.”
Reliability Learned in the Physical World
That instinct for how systems fail was shaped long before the current AI boom, in the unforgiving world of consumer hardware. Physical products punish unreliability in public: U.S. consumer electronics companies spend close to $17 billion a year processing returns, and studies have found only about 5 percent of returned devices have any real defect, while roughly one in seven phones is reported faulty within its first year. Catching problems before a product ships is not a nicety; it is the difference between a trusted brand and an expensive recall.
Early in his career, Reddy engineered reliability in at exactly that stage. He designed and built a robotics-driven platform that automated long-duration failure testing for consumer electronics, running continuous, unattended validation across more than 50 robotic stations around the clock, work that would otherwise have consumed weeks of repetitive human effort. He also built automation frameworks for flagship wireless audio devices, testing Bluetooth connectivity, audio synchronization, over-the-air firmware updates, and acoustic performance. The through-line to his work today is clear: he learned to make reliability a property of the system itself, proven with data, rather than something spot-checked by hand at the end.
“Hardware teaches you humility,” Reddy reflects. “A product either holds up in someone’s hands or it doesn’t, and you cannot argue with a device that fails. That is where I learned to build the proof of reliability into the process instead of hoping for it at the end.”
The Migration Underneath Everything
As software moved off company servers and into the cloud, reliability became a moving target. Worldwide spending on public cloud services reached an estimated $723 billion in 2025, up more than 21 percent in a single year, and Gartner expects 90 percent of organizations to run hybrid cloud environments by 2027. For most enterprises, that shift means untangling decades of aging systems and rebuilding them to run reliably at far greater scale.
Reddy has done that heavy lifting firsthand. He led a large-scale modernization effort that migrated more than 100 enterprise services and over 100 continuous-integration pipelines onto a scalable cloud-native architecture on Amazon Web Services, replacing brittle legacy infrastructure with containerized systems that were cheaper to run, easier to scale, and more resilient. That foundation is what makes his current AI work possible; intelligent diagnostics only function when the systems beneath them are consistent enough to be understood.
“You cannot put an intelligent layer on top of chaos,” Reddy notes. “Modernizing the infrastructure is not glamorous, but it is what lets everything above it, including the AI, actually work.”
Toward Systems That Heal Themselves
The next frontier is operational systems that do not just diagnose problems but resolve them. Early autonomous systems already hint at what that looks like: one agentic operations vendor reported that in 2025 its AI resolved 230,000 alerts on its own, saving an estimated 12,000 engineer hours. The shared goal across the field is software that can detect, understand, and repair many of its own failures with little human involvement.
That is the future Reddy is building toward. He plans to keep pushing on self-healing systems while mentoring engineers through the shift from traditional testing to AI-enabled system design, and he shares what he learns with the wider community through his writing on HackerNoon. As a Senior Member of the IEEE, he also helps review applications from other engineers seeking the same distinction, contributing to the standards that define achievement in his field.
“We spent years building systems that could only tell us they were broken,” Reddy concludes. “The next generation will tell us why, and increasingly, they will fix it themselves. The point was never to replace engineers. It was to give them back their time.”
