AgentSight
An open-source AI agent observability project based on eBPF technology. By attaching probes at the Linux kernel layer, it monitors AI agent runtime behavior in a zero-intrusion manner, capturing system-call-level activities including network communication, process creation, and file access.
Timeline (last 90 days)
AgentSight is a project that leverages eBPF technology to provide observability capabilities for AI agents without modifying any application code.
AgentSight made its debut in the Show HN section of Hacker News.
AgentSight captures agent behavior at the system call level by mounting probes at the kernel layer, including network communication, process creation, and file access.
AgentSight's kernel-level observation approach enables monitoring of agents regardless of the programming language or framework they use, with zero intrusion into business logic.
AgentSight is applicable to use cases such as agent security auditing, debugging and performance analysis, and production environment compliance monitoring.
What is visible at the kernel layer consists of low-level signals such as system calls; mapping these back to the high-level decision semantics of agents requires additional correlation and interpretation work, presenting a semantic gap.
All Facts (5)
AgentSight is a project that leverages eBPF technology to provide observability capabilities for AI agents without modifying any application code.
50%UnverifiedAgentSight captures agent behavior at the system call level by mounting probes at the kernel layer, including network communication, process creation, and file access.
50%UnverifiedAgentSight's kernel-level observation approach enables monitoring of agents regardless of the programming language or framework they use, with zero intrusion into business logic.
50%UnverifiedAgentSight is applicable to use cases such as agent security auditing, debugging and performance analysis, and production environment compliance monitoring.
50%UnverifiedWhat is visible at the kernel layer consists of low-level signals such as system calls; mapping these back to the high-level decision semantics of agents requires additional correlation and interpretation work, presenting a semantic gap.
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