Gumloop Raises $50M from Benchmark to Make Every Employee an AI Agent Builder
Key Facts
- What: Gumloop secured $50 million in Series B funding led by Benchmark to expand its no-code AI agent platform
- When: Announced March 12, 2026; company founded in mid-2023
- Investors: Benchmark (lead), with participation from Nexus VP, First Round Capital, Y Combinator, Box Group, The Cannon Project, and Shopify
- Customers: Includes Shopify, Ramp, Gusto, Samsara, Instacart, and Opendoor
- Differentiation: Model-agnostic platform enables non-technical employees to build and share reliable, multi-step AI agents with minimal learning curve
Lead paragraph
Gumloop, a no-code platform that lets any employee build and deploy autonomous AI agents, has raised $50 million in Series B funding led by Benchmark. The investment reflects growing enterprise demand for tools that turn knowledge workers into AI agent creators without requiring engineering support. Co-founder and CEO Max Brodeur-Urbas says the platform helps organizations rapidly scale internal automation as teams share agents across departments, creating what he calls an “AI native” company.
Company Origins and Evolution
When Max Brodeur-Urbas co-founded Gumloop in mid-2023, AI agents were still largely experimental and unreliable. The company’s original mission was to help non-technical employees automate repetitive tasks using AI. As large language models matured, Gumloop evolved into a full agent-building platform capable of handling complex, multi-step workflows.
The platform allows users to drag, drop, and connect modular components onto a canvas to create end-to-end automations. Features include an “Agent Builder” that lets users “vibe code” agents from natural language prompts and an “Autopilot” capability that enables agents to use cloud-based computers, moving beyond strict API limitations.
Customers now include major organizations such as Shopify, Ramp, Gusto, Samsara, Instacart, and Opendoor. According to the company, these teams deploy reliable AI agents that operate without ongoing engineer involvement. Employees can share the agents they build with colleagues, generating a compounding effect that accelerates company-wide automation.
“They get addicted, they start building more agents, and then all of a sudden, the whole company is AI native,” Brodeur-Urbas told TechCrunch.
Benchmark’s Bet on Enterprise Automation
Everett Randell, who joined Benchmark as a general partner last October after a stint at Kleiner Perkins, led the $50 million round. It marks his first investment at the storied firm, whose portfolio includes eBay, Uber, and Dropbox. Additional participants include Nexus VP, First Round Capital, Y Combinator, Box Group, The Cannon Project, and Shopify.
Randell believes the key to successful AI adoption lies in giving every worker “AI superpowers.” He views Gumloop’s intuitive agent builder as a prime example of the tools needed to achieve that goal.
During due diligence, Randell discovered that at least one customer had adopted Gumloop organically. When he asked the company’s CTO why they chose the platform, the answer was revealing: employees had been given access to Gumloop alongside two competing tools. Six months later, staff were using Gumloop daily or weekly while the competing products remained largely untouched.
The reason, according to Randell, is Gumloop’s minimal learning curve. “You can go in and start making agents and workflow automations immediately,” he said.
Competitive Landscape
Gumloop operates in a crowded market. It competes with established automation platforms like Zapier and n8n, as well as newer specialized agent builders such as Dust. Even foundational AI labs are entering the space — Anthropic’s Claude Co-Work, for example, allows users to create autonomous agents without writing code.
Despite the competition, Randell argues Gumloop stands out due to its model-agnostic design. Rather than being tied to a single large language model provider, Gumloop lets users select the best model for each specific task. This flexibility offers both performance and cost advantages.
“Plenty of enterprises have OpenAI, Gemini, and Anthropic credits. They want to use all of them,” Randell told TechCrunch.
He sees enterprise automation as an enormous opportunity. “Enterprise automation is a massive pot of gold,” Randell said. “I think it’s the biggest category in enterprise AI.”
Growth Plans and Market Opportunity
Although Gumloop was not actively seeking new capital, surging demand from enterprise clients convinced the team that 2026 was the year to “step on the gas.” Brodeur-Urbas, who once envisioned building a lean 10-person billion-dollar company, is now focused on scaling both sales and engineering teams.
The funding will support the expansion of a dedicated sales force and additional engineering hires to meet customer demand and accelerate product development.
Gumloop positions itself as a platform that makes AI-powered automations cost-effective, reliable, and intuitive for anyone to build. Its integration capabilities allow connections to tools like Salesforce, Slack, and HubSpot, enabling data movement and centralized automation.
Impact
“They get addicted, they start building more agents, and then all of a sudden, the whole company is AI native.”
This quote from Brodeur-Urbas captures the viral adoption pattern many organizations are experiencing. For developers and IT teams, the platform reduces the burden of building internal tools by empowering business users to create their own automations. For non-technical employees in marketing, operations, growth, and other functions, it represents a significant shift in capability — turning complex workflows that once required engineering tickets into self-service projects.
The model-agnostic approach also has financial implications. Companies can optimize costs by routing different tasks to the most economical or performant model available at any given time, rather than being locked into a single provider’s pricing and capabilities.
In a market where many AI startups fear commoditization by foundational models, Gumloop’s bet is that the interface, reliability, and sharing capabilities matter more than any single underlying model. Benchmark’s investment suggests a leading venture firm agrees with that thesis.
What’s Next
With fresh capital and strong enterprise traction, Gumloop is expected to accelerate both product development and go-to-market efforts. The company has not disclosed specific product roadmap details or a timeline for new features.
The broader trend points to continued investment in tools that democratize AI agent creation. As models improve, the companies that can best translate that raw capability into reliable, easy-to-use business automation are likely to capture significant value.
For now, Gumloop’s success will be measured by how deeply it can embed itself into enterprise workflows and how effectively it can maintain its usability advantage against both specialized competitors and offerings from major AI labs.