You create a system-level AI tracing tool using eBPF to understand how AI agents work.
Automations like: trace how a new customer signed up on Stripe and how the AI-powered support chat responded.
With system-level AI tracing, you get detailed insights into AI decision-making without modifying the code. This helps you identify biases and areas for improvement.
"A founder wants to ensure that the AI-powered customer support chatbot on their website is working fairly for all users. They use agentsight to trace the flow of customer information through the AI system, identify biases, and make adjustments accordingly."
Start here if you're interested in exploring the basics of system-level AI tracing without diving into code.
This tool is useful for senior engineers looking to optimize and fine-tune the performance of AI agents in production environments.
Agentsight is not about instrumenting every line of code, but rather a zero-instrument approach that integrates with eBPF to provide system-level AI tracing insights.
Agentsight provides system-level insights into AI agents using zero instrument tracing with eBPF, allowing founders to monitor and optimize their AI systems without modifying the code.
git clone https://github.com/eunomia-bpf/agentsight.gitsudo ./agentsight start -f ./examples/config.yaml -o ./output
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