Traditional monitoring can confirm that servers are healthy yet still leave engineers blind to why an AI agent made a wrong decision. Ayush Jain, a software engineer who has built large-scale search, machine learning, and distributed systems, explains why observability and evaluation are becoming the foundation of trustworthy AI at enterprise scale.
An AI agent finishes its task. Every API call returns cleanly, logs are green, dashboards show no errors—yet the answer is wrong. Nobody can explain why. This gap between a system that looks healthy and one that actually performs correctly is the problem Jain has spent his career addressing. He belongs to a group of engineers building the unglamorous layer beneath enterprise AI: the pipelines, traces, and telemetry that let teams see what autonomous systems are really doing.
“Traditional monitoring can confirm that infrastructure is healthy,” Jain says, “but it often fails to explain why an AI system made a particular decision or produced an unexpected outcome.”
For two decades, deterministic software left clear fingerprints when it broke: error logs, exceptions, failed health checks. Agentic systems do not work that way. They reason in probabilities, select their own tools, pull in outside context, and plan across multiple steps. These agents introduce entirely new failure modes. “An agent may successfully execute API calls while still producing incorrect outcomes due to flawed reasoning, poor tool selection, or incomplete context,” Jain adds. The pipes all work, but the judgment does not. Standard monitoring stacks, built to watch the pipes, never notice the failure.
Jain learned this lesson at scales where small problems become expensive. At Bloomberg, he contributed to search and ranking systems supporting hundreds of thousands of user interactions across more than one hundred million documents. A search result that drifts a few points in relevance does not throw an exception, but at that volume it quietly degrades the experience for thousands. To catch such drift, he helped build observability pipelines that processed millions of telemetry events per day—turning raw system exhaust into real-time signals about search quality, user behavior, performance, and anomalies. The goal was not just to confirm machines were up, but to close the distance between an idea that worked in an experiment and a system that held up in production.
At Microsoft, Jain has focused on AI agent platform infrastructure. The central challenge: failures are now behavioral rather than infrastructural. An agent that calls every API correctly can still choose the wrong tool, reason poorly, or act on incomplete context. So the platform must watch behavior, not just uptime. The systems he worked on capture execution traces, monitor how workflows resolve, collect telemetry about agent conduct, and let teams run the same task across different configurations to see which performs best. This is the difference between knowing a process finished and knowing it finished for the right reasons.
Jain sees the industry shifting from optimizing model performance to optimizing whole AI systems. “I believe the industry is shifting from optimizing model performance to optimizing AI systems,” he says. For years the scoreboard was the model—a higher benchmark, a better accuracy number. But a benchmark is a single moment, while an agent makes sequences of decisions across tools and steps. Accuracy and precision stop describing what matters. “Agent behavior must be evaluated continuously rather than treated as a one-time model validation exercise,” Jain emphasizes. The metrics that count look less like test scores and more like operations reports: task completion, reasoning quality, tool effectiveness, cost, safety, and alignment with business intent.
He draws an analogy with cloud computing and Site Reliability Engineering (SRE). “Enterprise AI is entering a phase similar to what cloud computing experienced during the rise of Site Reliability Engineering,” Jain explains. Cloud services eventually stopped being judged solely on speed and started being judged on uptime and latency that everyone could see. AI will follow the same arc, with a new vocabulary of behavioral measures: hallucination rates, reasoning consistency, retrieval effectiveness, policy adherence, and workflow completion. “I believe AI Reliability Engineering will become a foundational discipline,” he says. Companies that build it into their platforms early will hold a real advantage over those that bolt it on after something breaks.
The attention has long gone to models and clever outputs. But Jain argues the durable problem sits one layer down: in whether anyone can explain, measure, and trust what the system did. “The challenge is no longer simply generating intelligent outputs—it is creating systems that make those outputs explainable, measurable, and trustworthy at scale.” He expects the next generation of enterprise platforms to make observability and evaluation first-class architectural components rather than afterthoughts, with online evaluation, execution traces, and feedback loops catching trouble before users do.
His conclusion is plain: “Making AI systems measurable, explainable, and reliable is essential for successful enterprise adoption at scale.” The companies treating that as the real engineering challenge are the ones whose AI will still be trusted a year after the demo. The rest are flying on green dashboards, learning the hard way that healthy is not the same as right.
