Why now
Enterprise software is being handed to agents faster than companies can supervise them. Gartner expects 40 percent of enterprise applications to be integrated with task-specific AI agents by the end of 2026, up from less than 5 percent in 2025. Yet a Deloitte survey of 3,235 business and technology leaders in 24 countries, published in April 2026, found that only 21 percent of organizations have a mature governance model for agentic AI. The consequences are already visible in outage records: an August 2026 analysis of roughly 178,000 public status-page incidents found that AI-related incidents now account for more than one in ten reported outages, a sixfold increase since 2023. With high-impact outages costing the median large business about 76 million dollars a year, or roughly 2 million dollars an hour, the question of whether an autonomous system can explain and justify what it is doing has stopped being academic.
That is the problem Vaishnavi Yeruva has spent five years solving inside some of the largest companies in the United States. A Senior Consultant in AI Engineering at Deloitte Consulting LLP in Austin, Texas, Yeruva designs and deploys agentic AI systems that do not simply flag that something changed, but investigate why, and carry their evidence with them.
Diagnosis at the scale of hundreds of millions of users
Yeruva's most consequential work to date was carried out between 2022 and 2026 for a Fortune 500 social media company, where she was embedded through Deloitte as a data scientist responsible for investigating critical network and video performance issues across the telecommunications ecosystems that serve its apps, products used by hundreds of millions of people. Root-cause analysis in an environment of that size had traditionally depended on senior engineers writing bespoke queries by hand, often for days, before a performance problem in one region could be traced to a network, application or partner cause.
Yeruva designed and deployed an agentic AI diagnostics system for root-cause analysis that embeds expert investigative reasoning into semi-autonomous workflows. The system queries multiple data sources on its own, systematically narrows hypotheses, correlates signals across very large datasets, and proposes corrective strategies, all without a human writing a line of SQL. Alongside it she architected scalable analytics pipelines, new metrics for short-form video products, and automated data-quality monitoring. The results were measurable for the organization: data utilization rose by 97 percent, and data integrity and operational responsiveness improved by 95 percent. The investigations her system supports inform the company's network optimization and capital investment decisions with its global telecom partners.
"Dashboards tell you that something changed. They almost never tell you why," Yeruva says. "The why used to depend on a person writing the right query at two in the morning. That is the part we automated, and we automated it in a way that a domain expert can audit line by line."
From a week to a day
Since 2026 Yeruva has served as the sole onshore agentic AI architect on a Deloitte engagement with a Fortune 500 financial services firm, where the assignment was not diagnosis but delivery. She re-engineered the client's software development lifecycle, its Git, continuous-integration and deployment pipelines, around agentic orchestration built on Claude Code and Model Context Protocol integrated development patterns. She owned the archetype design, the observability layer and the guardrails, and coached the pilot teams through hands-on adoption.
The outcome for the client was a change in kind rather than degree: feature delivery cycle time fell from about one week to about one day, and the engineering headcount required per workstream fell from twelve to four, a threefold gain in capacity. For Deloitte, the engagement has become a reference model for what an AI-powered delivery organization looks like, and the reusable frameworks Yeruva established have been adopted on other engagements.
Earlier deployments in healthcare
Yeruva's first Deloitte engagements, in 2021, were in healthcare. For a Fortune 500 healthcare company she built end-to-end machine learning pipelines that flagged patients at risk of chronic conditions before escalation, enabling earlier clinical intervention across large member populations, and worked with product and strategy teams to convert model outputs into deployed care-outreach programs. For a Fortune 500 health insurer, she embedded explainable AI into enterprise healthcare software to surface health-equity risks and power transparent, personalized preventive-care recommendations, deployed in direct partnership with clinicians. The common thread with her later work is the same insistence: a model that cannot justify its answer is a model that cannot be shipped.
Roots in a speech laboratory
That insistence was formed in research. Before consulting, Yeruva spent three years in Indian speech laboratories, first as a Research Associate at PES Institute of Technology in Bangalore and then as a Research Assistant in the LEAP laboratory of the Indian Institute of Science, ranked India's top research institution in the Government of India's NIRF 2025 rankings. She published five peer-reviewed papers at INTERSPEECH, Odyssey, ICANN, IEEE INDICON and India's National Conference on Communications, presented her work in India and Italy, and was part of IISc's team in the NIST Language Recognition Evaluation 2017, an international benchmark run by the United States National Institute of Standards and Technology. Her multitask learning approach improved wake-word detection by about 30 percent over the baseline, and her spoken language identification work improved on the existing approach by about 22 percent. In 2016 she was selected to speak at the Grace Hopper Celebration India, Asia's largest gathering of women and allies in technology. She later completed a Master of Science in Artificial Intelligence at Northwestern University.
"Speech is a brutal teacher," she says. "The signal is noisy, ambiguous and human. You learn quickly that a model that cannot justify its answer is a model you cannot ship."
Influence beyond the engagement
Yeruva's influence now extends past any single project. She serves as a technical interviewer who evaluates AI engineering candidates for Deloitte and for client teams, and she is preparing two peer-reviewed articles, a perspective on the line from associative-memory networks to agentic reasoning and an evaluation framework for explainable agentic AI in enterprise operations. The evaluation framework addresses precisely the governance gap the Deloitte and Gartner figures describe: how to grade an autonomous analyst before it is trusted with an enterprise's infrastructure.
The industry spent a decade teaching computers to see patterns humans miss. Yeruva's contribution has been to make them explain those patterns well enough that a company will act, and spend, on the machine's word. In the systems she has built, the diagnosis arrives as a chain of reasoning that a domain expert can audit: which data sources were consulted, which anomalies were weighed, which alternative explanations were considered and rejected. "Autonomy without an audit trail is just a faster way to be wrong," she says. Her record suggests the industry is beginning to agree.