How Replit Made Its AI Agent 10X More Autonomous in a Single Leap

AI coding platform Replit just raised $250 million, tripling its valuation to $3 billion. But the real story isn’t just the money. It’s the launch of Agent 3, a next-generation AI developer that can build, test, and debug applications almost entirely on its own.

In a bold claim, CEO Amjad Masad called it the “full self-driving moment of software,” stating that Agent 3 is 10 times more autonomous than its predecessor and can run for over three hours without human intervention.

To understand what this leap in autonomy means and why it’s a critical signal for the future of software development, I turned to Marketing AI Institute founder and CEO Paul Roetzer on Episode 167 of The Artificial Intelligence Show.

A New Era of Autonomous AI

Replit’s Agent 3 isn’t just another code suggestion tool. It represents a significant step toward true AI autonomy in software development. Unlike earlier versions, Agent 3 doesn't just write code. It actively tests the applications it builds, identifies bugs, and fixes them, mimicking the workflow of a human developer.

This breakthrough is a core part of Replit's strategy, which has driven staggering growth. The company’s annual revenue skyrocketed from $2.8 million to $150 million in under a year, fueled by a user base of 40 million and enterprise clients like Zillow and Duolingo.

Replit’s claim of “10X more autonomous” is based on runtime, or how long the agent can operate without getting stuck. While Agent 2 could run for about 20 minutes, Agent 3 can now operate for over 200 minutes straight.

“The way these labs are thinking about the future of AI development and the impact it’ll have on society and the economy is: How long can these things work reliably without human intervention?,” says Roetzer.

This is different from how other organizations, like METR (Model Evaluation and Threat Research), measure AI capability. METR’s “seven-month rule” suggests that every seven months, AI doubles the complexity of a task it can complete successfully in a given time. For example, in March 2025, top AI models had a 50% chance of completing a coding task that would take an expert human one hour.

Replit, however, is achieving its exponential gains by using a multi-agent architecture, according to Masad. Instead of relying on a single model, it uses different AIs for different tasks: one for planning, one for coding, and another for verification.

“What he’s saying is we actually rapidly scale beyond this by using multiple agents,” Roetzer says. 

What This Means for the Future of Work

While Replit’s breakthroughs are currently focused on coding and software development, they offer a glimpse into the future of knowledge work across all industries. The scaling laws we’re seeing in programming today may eventually be applied to marketing, finance, healthcare, and operations.

“The key for the economy is: When do we see these kinds of scaling laws in healthcare, in finance, in marketing and sales, and customer success and operations?,” Roetzer notes. “We don't have that research right now.”

The development of highly autonomous AI agents raises critical questions about the future of jobs. When an AI can perform the work of a human developer for hours on end, the nature of these roles will inevitably change.

For now, the most advanced research is centered on AI and engineering because that’s where the most immediate value can be created. But these advancements are a prelude to a much broader transformation.

“The breakthroughs happen where the greatest value will be created, which right now is in AI research and engineering,” says Roetzer. “And that’s why all the labs are starting there. But when they solve how to do it there in reliable ways, then it trickles out into all the other industries.”

The Bottom Line

Replit’s Agent 3 is both an impressive tech breakthrough and a larger indicator of where the AI industry is headed. The focus on increasing autonomous runtime is a race to create AI systems that can handle complex, multi-step tasks with minimal human oversight. As these capabilities expand beyond coding, the impact on the workforce will be profound. 

“All of this is a prelude to what will happen in the general tools the rest of us use,” says Roetzer.