Inferensys

Glossary

eBPF

A revolutionary kernel technology that allows sandboxed programs to run in the Linux kernel without changing kernel source code, enabling deep, high-performance observability and security enforcement for agent processes.
Developer demonstrating multi-agent tool use, agent tool selection interface on laptop, casual tech demo moment.
EXTENDED BERKELEY PACKET FILTER

What is eBPF?

eBPF is a revolutionary kernel technology that allows sandboxed programs to run in the Linux kernel without changing kernel source code, enabling deep, high-performance observability and security enforcement for agent processes.

eBPF is a technology that enables the dynamic injection of custom, sandboxed programs directly into the Linux kernel at runtime. It fundamentally changes the operating system's behavior without requiring kernel source code modifications or rebooting. By attaching to kernel hooks and events, eBPF programs can safely and efficiently inspect, filter, and manipulate system-wide data, including network packets, system calls, and function entry points, all within a strictly verified execution context.

For autonomous agent sandboxing, eBPF provides a powerful, non-invasive mechanism for runtime security and deep observability. It allows a Policy Enforcement Point to monitor agent processes at the syscall level, enforcing Least Privilege Execution by detecting and blocking anomalous behaviors like unauthorized file access or network connections. This kernel-level visibility, operating below the container or MicroVM, is critical for detecting Container Escape attempts and implementing fine-grained Egress Filtering without the performance overhead of traditional security agents.

KERNEL OBSERVABILITY

Key Features of eBPF

eBPF revolutionizes Linux kernel programming by enabling sandboxed, high-performance programs to run directly in the kernel without modifying source code. These capabilities make it the foundation for next-generation observability, security, and networking for autonomous agent infrastructure.

01

In-Kernel Sandboxed Execution

eBPF programs run in a strictly sandboxed virtual machine inside the Linux kernel. Before loading, every program passes through a verifier that statically analyzes the bytecode to guarantee safety: no infinite loops, no out-of-bounds memory access, and no unauthorized kernel state modification. This ensures that even a misbehaving eBPF program cannot crash the kernel or compromise system stability, making it safe for production agent environments.

< 10 µs
Verification Time
Zero
Kernel Crashes
02

Just-In-Time Compilation

eBPF bytecode is translated into native machine instructions by a JIT compiler in the kernel. This means eBPF programs execute at near-native CPU speed, avoiding the overhead of interpretation. For agentic systems requiring real-time syscall monitoring or network packet filtering, JIT compilation ensures observability instrumentation adds negligible latency to critical paths.

~1 ns
Per-Event Overhead
Native
Execution Speed
03

Dynamic Kernel Instrumentation

eBPF enables runtime attachment to predefined hooks throughout the kernel without rebooting or recompiling. Key hook types include:

  • kprobes/kretprobes: Dynamic tracing of any kernel function entry or return
  • tracepoints: Stable, pre-defined instrumentation points in kernel subsystems
  • uprobes: User-space function tracing for agent process introspection
  • USDT probes: User-level statically defined tracing markers This allows DevSecOps teams to instrument agent behavior on demand.
04

Efficient Data Structures: eBPF Maps

eBPF programs use maps to store and share data between kernel and user space. These are generic key-value data structures with various backends optimized for different access patterns:

  • Hash maps for fast lookups
  • Ring buffers for lossless, high-throughput event streaming
  • Per-CPU arrays for lock-free concurrent access
  • LRU maps for bounded memory usage with automatic eviction Maps enable stateful monitoring of agent syscall patterns across multiple events.
O(1)
Hash Map Lookup
Lock-Free
Per-CPU Access
05

Helper Functions and Tail Calls

eBPF programs are restricted from calling arbitrary kernel functions for safety. Instead, they invoke a curated set of helper functions that provide controlled access to kernel capabilities:

  • bpf_get_current_pid_tgid() to identify the calling process
  • bpf_perf_event_output() to stream data to user space
  • bpf_tail_call() to chain programs together without returning to user space Tail calls enable modular, composable eBPF logic for complex agent security policies.
06

CO-RE: Compile Once, Run Everywhere

Compile Once, Run Everywhere (CO-RE) solves the portability challenge of eBPF across different kernel versions. It uses BTF (BPF Type Format) metadata to relocate field offsets and structure layouts at load time. A single compiled eBPF object can run on kernels from 5.4 to the latest release without recompilation, dramatically simplifying deployment across heterogeneous agent host fleets.

5.4+
Kernel Support
Single
Binary per Program
eBPF DEEP DIVE

Frequently Asked Questions

Explore the core concepts of eBPF, the revolutionary kernel technology that enables sandboxed programmability for high-performance observability and security enforcement in agentic systems.

eBPF (extended Berkeley Packet Filter) is a revolutionary technology that allows sandboxed programs to run in a privileged context, such as the Linux kernel, without changing kernel source code or loading kernel modules. It works by enabling developers to write programs in a restricted C-like language, which are then compiled to eBPF bytecode. A verifier statically analyzes this bytecode to guarantee safety—ensuring no infinite loops, out-of-bounds memory access, or crashes—before a JIT (Just-In-Time) compiler translates it into native machine instructions for maximum performance. These programs attach to various kernel hooks, like system calls, network events, or tracepoints, allowing for deep, high-speed observability and enforcement directly in the kernel's execution path.

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.