I spent 2 months building observability for AI voice agents because debugging them was driving me insane
Summary
Developer built VoiceOBS, an observability tool for AI voice agents, providing latency breakdowns, sentiment analysis, hallucination detection, and more, integrated with Vapi.
Similar Articles
Built my own voice AI platform after Vapi burned me. Wrote up everything I learned shopping for one.
The author shares lessons from building their own voice AI platform after dissatisfaction with Vapi, revealing hidden costs, real-world latency issues, and white-label shortcomings, and offers a free guide for agency owners evaluating platforms.
Five observability gaps we keep seeing in production voice AI stacks
Discusses five common observability gaps in production voice AI stacks, including blending infrastructure and conversation failures, lack of VAD visibility, inadequate sampling, noisy auto-generated evals, and evaluating at the wrong level.
Open Source Profiler for Voice Agents - Understanding from inside
An open source profiler designed for voice agents to provide insights into internal operations and performance.
AI voice agents look impressive in demos. Has anyone actually deployed one in production? What broke?
The article questions whether AI voice agents have been successfully deployed in production beyond impressive demos, highlighting the gap between demo performance and real-world reliability.
How to go about evaluation and Observability while building AI agents?
The author discusses challenges in evaluating and monitoring AI agents in production, including offline vs online evals, LLM-as-a-judge, tracing, and cost tracking, while citing tools like Langfuse and LangSmith but focusing on underlying processes.