Kernel Bees offers a CUDA‑accelerated framework that runs autonomous AI agents directly at the operating‑system kernel level, reducing data‑transfer overhead and improving GPU utilization. By providing low‑latency primitives such as agent scheduling, fast memory context switching, and hardware‑accelerated tool calls, it enables developers to build real‑time, production‑grade agentic systems that can swarm hundreds of agents concurrently on a single GPU.
Funding
Funding not disclosed
Founders
Product
Problem
Current AI agent frameworks operate at the application layer, causing significant data-transfer latency, inefficient memory management, and fragmented GPU utilization when executing multi-step reasoning and tool calls. This overhead makes large-scale, real-time autonomous agent deployments slow and costly.
Solution
Kernel Bees provides a CUDA‑accelerated framework that runs autonomous AI agents directly on the operating system kernel, eliminating the need for costly data movement between user space and hardware. By integrating native CUDA libraries, it offers low‑latency scheduling, fast‑path memory context switching, and hardware‑accelerated tool execution on tensor cores. The framework parallelizes chain‑of‑thought processing, allowing hundreds of agents to operate concurrently with efficient VRAM usage. Developers can build production‑grade, real‑time agentic ecosystems that fully exploit GPU compute without the bottlenecks of traditional stacks.
Target Audience
Primary customers are AI developers and enterprises building large‑scale autonomous agent systems that require real‑time performance and efficient GPU utilization.
Features
- Kernel‑level execution engine that positions agent orchestration directly above the OS for minimal latency
- Native CUDA integration for optimized memory allocation and fast context switching across agents
- Custom CUDA kernels that enable parallel swarm processing of hundreds of agents on tensor cores
- Asynchronous hardware‑accelerated tool calling, reducing overhead of external API and embedding lookups
- Unified primitives for agent scheduling, memory management, and tool execution to simplify development of complex agent hives