Agent Kernel now offers comprehensive observability support through integrations with Langfuse and OpenLLMetry. This feature is crucial for understanding, debugging, and optimizing complex AI agent systems as they grow. Developers can now track every agent interaction, including LLM calls, tool invocations, and sub-agent communications in real-time. The system allows for confident debugging with detailed execution timelines and error tracking. It also helps optimize costs by monitoring token usage and LLM expenditures. Furthermore, quality assurance is enhanced with built-in analytics and metrics for agent performance. Production-ready monitoring provides alerts, dashboards, and insights for critical agent deployments. Agent Kernel's extensibility allows users to switch observability platforms with a single configuration change, and even integrate custom solutions. Langfuse offers specialized LLM analytics, prompt management, and evaluation tools, while OpenLLMetry leverages OpenTelemetry for flexible integration into existing monitoring infrastructure. Both options provide robust privacy and security features, including self-hosting capabilities. These integrations have minimal performance impact, adding less than 5% overhead to agent execution time. Developers can easily get started by installing the necessary packages and configuring their chosen platform.