Kernel

Kernel addressed a critical infrastructure gap: AI agents lacked reliable, fast access to the internet. ​​‌‌‌‌‌‌‌​‌‌​​‌​​​​​​‌‌​‌‌‌​​​‌‌Companies building autonomous agents—from research labs to enterprise automation teams—faced a brutal choice between slow, resource-heavy browser automation tools or building custom solutions from scratch. The problem hit hardest for teams running high-volume agent workloads, where latency directly impacted cost and performance. The pain was measurable: existing solutions like Selenium and Puppeteer suffered 5-10 second cold starts and consumed significant memory per instance. Alternatives existed but were inadequate—either general-purpose cloud infrastructure or specialized tools that required extensive engineering to scale. Early validation came from developer adoption patterns: the open-source release generated immediate GitHub traction from AI labs experimenting with web-browsing agents. The sub-150ms cold start performance directly addressed the most-cited bottleneck in agent frameworks. Teams building production systems quickly recognized that Kernel's unikernel architecture solved their core constraint: enabling agents to interact with dynamic web content at scale without prohibitive infrastructure costs.