I Built a Profiler to Audit My Own AI Tool Calls. Here's What I Learned About Observability
The author developed a profiler called AgentLens to improve observability in AI tool usage. After analyzing 157 skills, they identified inefficiencies such as redundant tool calls and silent failures. The article emphasizes the importance of observability in AI development over merely focusing on prompts.
- ▪The author built AgentLens to audit their AI tool calls after realizing a lack of visibility into their efficiency.
- ▪Common issues found included redundant tool calls, silent failures, and latency spikes.
- ▪AgentLens uses simple regex patterns to parse API logs and identify usage patterns.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3942046) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Ramagiri Tharun Posted on May 27 I Built a Profiler to Audit My Own AI Tool Calls. Here's What I Learned About Observability #ai #machinelearning #automation #buildinpublic I built a profiler to audit my own tool calls. After loading 157 skills in 12 days, I realized I had zero visibility into whether I was using them efficiently. So I built AgentLens. The Problem Nobody Talks About Most AI agent demos look magical because the demo is 30 seconds long.
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