Meet the Anthropic Engineer Keeping Its AI Agent Trustworthy

If you’ve ever wondered how Claude and similar AI agents can answer complex questions in seconds and where they get the information, you’re not alone.

Gurmeet Saran works to find that answer every day as an engineer at Anthropic in San Francisco, the AI research company behind Claude. The IEEE senior member is developing an infrastructure to understand the mechanics behind the chatbot’s behavior and to ensure its underlying architecture produces reliable outputs.

Gurmeet Saran

Employer: Anthropic in San Francisco

Title: Member of technical staff

Member grade: IEEE senior member

Alma maters: University of Kalyani, India; Birla Institute of Technology and Science, Pilani, India

“My job is to make sure the data is accurate, structured, and trustworthy, and that private data stays private,” he says. “As I learned earlier in my career, privacy can’t be a layer you bolt on—it has to be part of the architecture from the start.” If you can’t trust the logic, he adds, you can’t make reliable claims about the behavior, and that has real safety implications.

Safety is a priority for Anthropic, he says. According to Claude’s mission statement, the company aims for the AI agent to be “genuinely helpful to the people it works with or on behalf of, as well as to society, while avoiding actions that are unsafe, unethical, or deceptive.” A crucial part of this mission is ensuring that researchers can observe and understand how AI models behave, a challenge that is particularly acute for large language models, Saran notes.

“We have decent tools to monitor databases and web services,” he says, “but for LLMs, we’re still figuring it out.”

Solving Data Quality Problems at Yahoo, Intuit, and Meta

Saran’s interest in computing systems began in his childhood in Kolkata, where he spent significant time in the school computer lab.

“I remember the first time I used one of the computers,” he recalls. “What struck me was that you could write instructions, and the machine would follow them exactly. No ambiguity. That felt magical to me.”

After graduating from high school in 1999, he pursued advanced degrees in India, earning a bachelor’s degree in electronics and telecommunication engineering in 2004 from the University of Kalyani and a master’s degree in software engineering in 2010 from the Birla Institute of Technology and Science.

While a graduate student, he began his career in 2007 at Yahoo as a technical lead of the data team in Bengaluru, India. There, he encountered a project that shaped his career trajectory: working on web analytics and business intelligence reporting solutions.

“We kept running into a frustrating problem,” he recalls. “Different teams would calculate the same website metric—something like daily active site users—using slightly different logic, leading to conflicting answers. It sounds like a small issue, but when leadership makes decisions about resource allocation and product development using that data, not knowing which number is correct becomes a problem.”

The team built a central data repository with defined metrics, serving as a single source of truth. Saran created a model that defined what “daily active site users” means, detailing which events counted and how to handle edge cases.

With this new model, data teams across the company could rely on a standard method to determine daily active users for their projects, obtaining comparable results using the same logic.

From there, Saran and his team developed Yahoo! Digits, an internal web analytics tool used for all the company’s products.

Saran left Yahoo in 2010 to join Intuit as a staff engineer in Mountain View, California. He helped build the analytics infrastructure that unified data across the company’s products, including TurboTax and QuickBooks. During his tenure, Intuit developed a feature that detected fraudulent tax filings in real time, requiring Saran and other engineers to build a system that protected taxpayer data while adhering to strict U.S. Internal Revenue Service policies.

“This taught me that privacy and governance can’t be layers you bolt onto software after the fact,” he says. “They have to be part of the architecture from the start.”

Saran joined Meta (formerly Facebook) in 2016 as a lead software engineer in Menlo Park, California. The company faced controversy over its video metrics, publicly acknowledging an error that caused it to overstate the number of views for posted videos.

“This wasn’t a coding bug; it was an architectural problem,” Saran explains. He built a system similar to what he had developed at Intuit, where a single truth governed all video view metrics across the site.

That system, Metric360, eventually became part of the site’s architecture. By the time he left Meta in 2021, Metric360 was being used across the company to calculate video metrics.

After leaving Meta, he became a principal software engineer at Wealthfront, a financial services company in Palo Alto, California, where he helped build the company’s data infrastructure.

Building Trustworthy AI Systems at Anthropic

Saran joined Anthropic in 2025 as a member of the technical staff.

To describe the implications of his work, he uses a medical analogy: “Imagine a doctor monitoring a patient’s vital signs and making treatment decisions based on those numbers. What if one or a few of the medical devices used were slightly miscalibrated? Even if most readings are fine, it’s a serious problem because the difficult decisions happen in the margins.”

“It’s the same with AI systems. My job is essentially to ensure the instruments are giving accurate readings—that the data means what it says it means.”

His role involves high stakes and complex problems to solve.

“AI agents are especially tricky to observe,” he notes. “When a system is taking sequences of actions [generating conversational responses with different users], you need a way to capture what it did, what information it was working from, and whether outcomes matched what the end user expected. And you have to do all of that without exposing anyone’s private information—the systems are designed to recognize sensitive data and keep it protected as it flows through. It’s a much more demanding observability problem than anything I’ve worked on before.”

Solving for system reliability while AI is still in its early days of adoption is both Saran’s passion and the problem that keeps him up at night.

“I think making AI systems genuinely auditable and transparent is one of the more important engineering problems of the next decade,” he says, “and it doesn’t get talked about as much as model capabilities do.”

IEEE’s Role in Shaping AI Development

Saran joined IEEE in March 2025.

“After more than 20 years of building data infrastructure across organizations like Yahoo, Intuit, Meta, and Wealthfront,” he says, “I wanted to contribute back to the technical community through peer review, publishing, and engaging with the broader engineering research ecosystem.”

He believes IEEE can play an essential role in shaping the future of AI through its standards work.

“We have standards for everything, from building codes to software testing,” he states. “AI infrastructure needs something similar, and IEEE has the credibility and the reach to do that.”

Another issue IEEE can address is access: “IEEE operates in many countries where engineers don’t have easy pathways into the global professional community. That kind of connection matters, especially as AI development spreads beyond a handful of organizations in a few cities.”

 

Source: spectrum.ieee.org