Genspark hits $36M revenue in 45 days with autonomous AI agent platform and “vibe work” approach

Genspark has introduced its Super Agent platform that enables “vibe working“—a natural language approach to enterprise workflows that allows AI agents to operate autonomously without rigid predefined processes. The platform challenges traditional enterprise AI orthodoxy by prioritizing autonomous problem-solving over deterministic workflow execution, potentially reshaping how organizations approach complex business tasks.

What you should know: Genspark’s Super Agent represents a fundamental shift from structured workflows to autonomous AI operation across enterprise functions.

• The platform combines nine different large language models in a mixture-of-experts configuration, equipped with over 80 tools and 10+ premium datasets.
• The system operates on a classic agent loop: plan, execute, observe and backtrack, with particular emphasis on the backtracking capability for intelligent failure recovery.
• Genspark achieved $36 million in annual recurring revenue in just 45 days from launch, demonstrating rapid commercial adoption of autonomous agent platforms.

How it works: The platform ditches predefined workflows in favor of adaptive problem-solving that can handle unexpected situations and edge cases.

• “Workflow in our definition is the predefined steps and these kinds of steps often break on edge cases, when the user asks harder and harder questions, the workflow cannot hold,” explained Kay Zhu, CTO of Genspark.
• The system uses large language model judges to evaluate every agent session and attributes rewards to each step, feeding this data back through reinforcement learning and prompt playbooks for continuous improvement.
• Unlike established frameworks like LangChain or CrewAI that require structured workflow definition, Genspark’s architecture prioritizes autonomous problem-solving over deterministic execution paths.

Real-world applications: Users are pushing the platform beyond traditional business workflows into personal territory, revealing unexpected use cases.

• During a live demonstration at VB Transform 2025, the system autonomously researched conference speakers, created presentations, made phone calls and analyzed marketing data.
• The platform placed an actual phone call to VentureBeat founder Matt Marshall during the live presentation, with the AI agent attempting to negotiate a presentation time slot change.
• “Some of the Japanese users are using this to call to resign from their company. You know they don’t like the company, but they don’t want to call them again. and some of the people are using call for me agents to break up with their boyfriend and girlfriend,” Zhu noted.

The big picture: The concept of “vibe working” extends the natural language coding approach to broader enterprise operations, potentially creating competitive advantages for early adopters.

• “The vision is simple, we want to bring the Cursor experience for developers to the workspace for everyone,” said Zhu. “Everyone here should be able to do vibe working… it’s not only the software engineer that can do vibe coding.”
• The platform’s ‘less control, more tools’ philosophy represents a departure from traditional enterprise AI approaches that rely on rigid workflow structures.

Why this matters: Organizations that remain locked into rigid workflow thinking may be disadvantaged as AI-native companies embrace more fluid, adaptive approaches to knowledge work.

• The success signals that autonomous AI agents are moving beyond experimental phases into commercial viability for complex business tasks.
• Enterprises need to start architecting systems that can handle both predictable workflows and autonomous problem-solving, designing platforms that gracefully escalate from deterministic processes to agentic behavior when complexity demands it.