Hey everyone,

If you’ve been building or deploying automated AI agents recently (using frameworks like CrewAI, Autogen, LangChain, or custom background LLM loops), you’ve probably experienced some version of the "infinite loop trap".

A few weeks ago, an unhandled formatting exception threw one of our background agent scripts into a tight infinite loop. It sat there spamming identical prompt contexts and API requests over and over again.

By the time we caught it, it had completely pinned the host CPU, locked up our local staging infrastructure, and racked up a massive cloud API bill. Standard APM tools and infrastructure monitors poll metrics every 10 to 30 seconds. That is an eternity when a rogue process is actively hemorrhaging API credits or stalling a compute node.

We needed something that could intercept and mitigate this instantly at the lowest level possible.So, I built KernelCap. It’s an open-source, ultra-low-overhead background daemon written in Go that hooks directly into the Linux kernel to catch and freeze rogue AI workloads before they tank your systems or your wallet.

Here is a breakdown of how it works under the hood:

1. Low-Overhead Kernel Tracking (eBPF)Instead of constantly polling the /proc filesystem (which gets incredibly heavy under load), KernelCap attaches Compile-Once Run-Everywhere (CO-RE) tracepoints and kprobes directly to Linux kernel ring buffers. This lets us monitor raw process scheduling and resource metrics with virtually zero system noise. The entire Go daemon maintains a strict memory ceiling of under 15MB RAM.
2. Monotonic Leak Detection (OLS Regression)AI workloads naturally cause massive spikes in CPU and GPU utilization during normal prompt processing. To prevent annoying false-positive alerts, KernelCap doesn't rely on generic threshold limits. Instead, it pipes incoming telemetry data arrays into an internal Ordinary Least Squares (OLS) linear regression model to calculate the directional slope of resource usage. It only flags a problem if it detects a true, continuous monotonic memory or thread leak ($R^2 > 0.95$).
3. Token-Loop Interception (SimHash Middleware)To catch actual infinite generation loops within text streams, KernelCap spins up an inline reverse proxy middleware. As text packets flow through the proxy, a Go-based SimHash engine hashes the data and calculates bitwise Hamming distances on the flying strings. If it detects highly repetitive semantic patterns repeating within milliseconds, it instantly trips a loop flag.
4. The OS-Level Circuit BreakerThe second the analytics engine flags a runaway loop or a structural host leak, it doesn't just drop an alert into a Slack channel to get ignored by a tired engineer. The Go daemon invokes a low-level syscall wrapper to fire a POSIX SIGSTOP signal directly at the exact target PID. This instantly freezes the process's CPU and GPU utilization to absolute zero. The container or application state stays allocated in memory so nothing crashes, but execution is entirely halted. You can triage the logs, fix the underlying code, and send a SIGCONT signal to safely resume the process when you're ready.

I spent a lot of time making sure this was easy to drop into a local environment. The repository includes:kernelcap doctor: A CLI utility built with spf13/cobra that runs automated pre-flight checks on your environment (root capabilities, BTF kernel map compatibility, and port availability) before enabling any active proxy routes.A Local WebSocket Dashboard: The engine relies on a loose EventRouter interface that streams real-time telemetry from the daemon to a lightweight, zero-dependency local web UI running at a smooth 60 FPS.

Check out the source code, architecture blueprints, and documentation over on GitHub: