Uploaded February 2026 | Updated September 2026, 3 days ago
This video tackles a critical challenge in AI agent development: understanding what's actually happening when agents behave unpredictably. Using a real-world example where two identical requests to the same AI agent produced dramatically different results—one taking 10 seconds with 10 operations, the other over a minute with 42 operations—the video demonstrates why observability is essential for agentic systems. Since LLMs decide their own execution paths, choosing which tools to call and how many times to loop, traditional debugging approaches fall short.
The solution presented is OpenTelemetry tracing, the same technology used to monitor distributed systems, now applied to AI agents. The video explains how OTel's standardized `gen_ai.*` semantic conventions capture AI-specific telemetry across any model provider, while integrating seamlessly with existing infrastructure observability. Through a hands-on demonstration using Jaeger and a Kubernetes-based AI agent, viewers learn how to trace the complete journey of requests through agents, LLMs, tool executions, and external services. The key takeaway: while AI-specific tools like LangSmith have their place for prompt debugging and evaluations, OpenTelemetry provides a unified, vendor-neutral standard that connects AI traces to everything else in your stack—giving you the visibility needed to debug, optimize, and trust your agentic systems.
#OpenTelemetry #AIAgents #Observability
Consider joining the channel: youtube.com/c/devopstoolkit/join
▬▬▬▬▬▬ 🔗 Additional Info 🔗 ▬▬▬▬▬▬
➡ Transcript and commands: https://devopstoolkit.live/observability/ai-agent-debugging-setup-opentelemetry-jaeger-in-kubernetes
🔗 OpenTelemetry: opentelemetry.io
🎬 Distributed Tracing Explained: OpenTelemetry & Jaeger Tutorial: youtu.be/Oa-zqv-EBpw
▬▬▬▬▬▬ 💰 Sponsorships 💰 ▬▬▬▬▬▬
If you are interested in sponsoring this channel, please visit https://devopstoolkit.live/sponsor for more information. Alternatively, feel free to contact me over Twitter or LinkedIn (see below).
▬▬▬▬▬▬ 👋 Contact me 👋 ▬▬▬▬▬▬
➡ BlueSky: https://vfarcic.bsky.social
➡ LinkedIn: linkedin.com/in/viktorfarcic
▬▬▬▬▬▬ 🚀 Other Channels 🚀 ▬▬▬▬▬▬
🎤 Podcast: devopsparadox.com
💬 Live streams: youtube.com/c/DevOpsParadox
▬▬▬▬▬▬ ⏱ Timecodes ⏱ ▬▬▬▬▬▬
00:00 Tracing for AI Agents
01:04 Why AI Agents Need Tracing
09:29 OTel vs AI-Specific Tools
This video tackles a critical challenge in AI agent development: understanding what's actually happening when agents behave unpredictably. Using a real-world example where two identical requests to the same AI agent produced dramatically different results—one taking 10 seconds with 10 operations, the other over a minute with 42 operations—the video demonstrates why observability is essential for agentic systems. Since LLMs decide their own execution paths, choosing which tools to call and how many times to loop, traditional debugging approaches fall short.
The solution presented is OpenTelemetry tracing, the same technology used to monitor distributed systems, now applied to AI agents. The video explains how OTel's standardized `gen_ai.*` semantic conventions capture AI-specific telemetry across any model provider, while integrating seamlessly with existing infrastructure observability. Through a hands-on demonstration using Jaeger and a Kubernetes-based AI agent, viewers learn how to trace the complete journey of requests through agents, LLMs, tool executions, and external services. The key takeaway: while AI-specific tools like LangSmith have their place for prompt debugging and evaluations, OpenTelemetry provides a unified, vendor-neutral standard that connects AI traces to everything else in your stack—giving you the visibility needed to debug, optimize, and trust your agentic systems.
#OpenTelemetry #AIAgents #Observability
Consider joining the channel: youtube.com/c/devopstoolkit/join
▬▬▬▬▬▬ 🔗 Additional Info 🔗 ▬▬▬▬▬▬
➡ Transcript and commands: https://devopstoolkit.live/observability/ai-agent-debugging-setup-opentelemetry-jaeger-in-kubernetes
🔗 OpenTelemetry: opentelemetry.io
🎬 Distributed Tracing Explained: OpenTelemetry & Jaeger Tutorial: youtu.be/Oa-zqv-EBpw
▬▬▬▬▬▬ 💰 Sponsorships 💰 ▬▬▬▬▬▬
If you are interested in sponsoring this channel, please visit https://devopstoolkit.live/sponsor for more information. Alternatively, feel free to contact me over Twitter or LinkedIn (see below).
▬▬▬▬▬▬ 👋 Contact me 👋 ▬▬▬▬▬▬
➡ BlueSky: https://vfarcic.bsky.social
➡ LinkedIn: linkedin.com/in/viktorfarcic
▬▬▬▬▬▬ 🚀 Other Channels 🚀 ▬▬▬▬▬▬
🎤 Podcast: devopsparadox.com
💬 Live streams: youtube.com/c/DevOpsParadox
▬▬▬▬▬▬ ⏱ Timecodes ⏱ ▬▬▬▬▬▬
00:00 Tracing for AI Agents
01:04 Why AI Agents Need Tracing
09:29 OTel vs AI-Specific Tools
