BrevFeed
Sign in
← All stories
● Covered by 1 source · 1 reportLow impact1 neutral

AgentSight Introduces eBPF-Based Observability Tool for AI Agents

🔄 Updated 2h ago
Aggregated by BrevFeed ai · updated 2h ago
🔖 Save
New to BrevFeed? We gather this story from every outlet covering it into one summary — ranked by real-world impact, not just the latest headline — so you never miss what matters. What is BrevFeed? →

Key points

  • AgentSight uses eBPF for zero-instrumentation AI agent observability.
  • It captures LLM API calls, token consumption, and process behavior.
  • The tool operates at the kernel level, requiring no code modifications.
  • Full-stack observability is provided for AI agents on Linux.

Introduction of AgentSight

AgentSight has launched a new observability tool specifically designed for AI agents. This tool utilizes eBPF technology to monitor the behavior of AI agents without requiring any modifications to their existing codebases. It focuses on capturing key operational data directly from the kernel.

Key Capabilities

The AgentSight tool is capable of capturing LLM API calls, monitoring token consumption, and observing process behavior. These capabilities are implemented at the kernel level, ensuring that the data collection is transparent and does not interfere with the agent's execution. This provides a detailed view of how AI agents interact with underlying systems and resources.

Platform Support and Installation

AgentSight offers full-stack observability for AI agents running on Linux. While macOS users can access a trajectory collector and dashboard viewer, eBPF-dependent commands are exclusive to Linux environments. Installation is available via the ANOLISA CLI, YUM repositories, or by building from source, with systemd unit files provided for normal deployments.

Operational Details

The service runs as root and stores data under /var/log/sysak/.agentsight. For troubleshooting, users can stop the systemd unit and run 'agentsight trace' and 'agentsight serve' commands separately in foreground. Localhost access to the dashboard is authentication-free, while remote access requires a token.

✨ This summary was generated by AI from the outlets' reporting listed below. It is not independently verified and may contain errors — check the original sources. How BrevFeed works →

Keep reading

ai Cloudflare Introduces Agent Tracing and Local Debugging for AI Agents on Workers 6d ago ai Amazon Bedrock Enhances AI Capabilities with Security and Operational Features 2h ago dev New Terminal Tools Manage AI Coding Agents and Sessions 2h ago dev Meta Introduces Hybrid Asset Classification for Privacy-Aware Infrastructure 2h ago security Researchers Reveal Security Flaws in AI Coding Agents and Open-Source Mobile Frameworks 23h ago

The daily brief

One email each morning: the day's tech stories, clustered across outlets and summarized. No account needed.

One email a day. Unsubscribe in one click, any time.

Today's brief

Spend a few minutes, get the whole day. Every topic's top stories in one hands-free rundown — listen, watch, or read the transcript.

~21 min · 18 stories · Aug 21

▶ Play today's brief Listen on Spotify

New every morning, and the back catalogue is archived by date.

Primary sources

GitHub cmc_internal/api GitHub github/collect GitHub alibaba/anolisa GitHub _private/browser GitHub get-started/accessibility GitHub open-source/sponsors

Reporting from

Hacker News Front Page — Show HN: AgentSight – eBPF observability for AI agents, no code changes 💬 Discuss on HN 5h ago →

AgentSight released a new observability tool for AI agents that uses eBPF to capture LLM API calls, token consumption, and process behavior at the kernel level without requiring code changes. This tool provides full-stack observability for AI agents running on Linux, offering insights into their operations.