kubernetesobservabilityPractitioner

Tracing AI Agents: Jaeger's Evolution with OpenTelemetry

5 min read CNCF BlogMay 26, 2026Reviewed for accuracy

Practitioner — Hands-on experience recommended

In the rapidly evolving landscape of AI, tracing and monitoring interactions between AI agents and their environments is crucial. Jaeger has stepped up to this challenge by evolving its architecture to natively integrate OpenTelemetry. This integration not only improves performance but also simplifies how engineers can trace AI agents, making it easier to understand their behavior and interactions in production.

Jaeger v2 has rebuilt its core architecture to utilize the OpenTelemetry Collector framework. This shift replaces previous collection mechanisms, consolidating metrics, logs, and traces into a unified deployment model. By adopting the Model Context Protocol (MCP), which standardizes how AI models access external data, and the Agent Client Protocol (ACP), which allows user interfaces to communicate with AI agents, Jaeger creates a collaborative environment for engineers and AI agents. The Agent–User Interaction Protocol (AG-UI) further facilitates this collaboration, ensuring seamless communication.

In production, the integration of OpenTelemetry means you can expect improved ingestion performance without the overhead of intermediate translation steps. This is particularly beneficial when dealing with complex AI systems where tracing interactions can become cumbersome. However, keep in mind that while Jaeger v2 offers powerful capabilities, it’s essential to understand the specific protocols and how they interact to fully leverage its potential.

Key takeaways

→Understand the Model Context Protocol (MCP) for secure data access by AI models.
→Utilize the Agent Client Protocol (ACP) for uniform communication with AI agents.
→Leverage the Agent–User Interaction Protocol (AG-UI) to enhance collaboration between engineers and AI agents.
→Adopt Jaeger v2 for improved performance through native OpenTelemetry integration.
→Consolidate metrics, logs, and traces into a unified deployment model for better observability.

Why it matters

In production, the ability to trace AI agents effectively can lead to faster debugging and improved system reliability, ultimately enhancing user experience and operational efficiency.

Code examples

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14mermaid
15graph LR
16  J_UI["Jaeger UI"]
17  AI_A["AI Agent"]
18  subgraph JAEGER["Jaeger v2"]
19    AGW["Agent Gateway"]
20    JMCP["Jaeger MCP"]
21  end
22 
23  J_UI -- "AG-UI Protocol" --> AGW
24  AGW -- "ACP Protocol" --> AI_A
25  AGW -- "MCP Protocol" &lt;--> JMCP
26)