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AI code generation is fast. Making it reliable requires runtime context. Today, Kiro gains live runtime visibility with the Lightrun MCP. This grounds AI-assisted development in how code actually behaves at runtime. Kiro, the AI coding assistant from the teams at AWS, is built for velocity and intuition. It moves from specification to production with speed and structure, helping teams turn intent into working code. But until now, like every AI coding assistant, Kiro had a major blind spot.

It has become common knowledge that the nature of software development has changed as AI-code generation and agent-based features gain adoption. In perhaps a more subtle shift, the fundamentals of software instrumentation are changing too. As OpenTelemetry becomes the standard instrumentation layer across enterprises, with thousands of developers (many from Honeycomb) actively contributing to it, the nature of the telemetry data captured itself is evolving to meet the growing demand for rich context.

Over the course of my 20 years as a developer, SRE, and now observability product leader, software has typically progressed at a good pace. But now, the emergence of two transformative technologies are fundamentally reshaping enterprise observability: generative AI (GenAI) and OpenTelemetry (OTel). We surveyed over 500 IT decision-makers for a new report:The Landscape of Observability in 2026: Balancing Cost and Innovation.

By incorporating observability tools into your stack, you can better understand how your complex infrastructure operates, reduce downtime, and empower developers to identify and fix problems quickly. However, it now takes considerably more work, time, and money to build the best observability tools for your infrastructure and applications. According to a Splunk survey, over half of the firms polled employ eight or more observability tools.

OpenTelemetry instrumentation is the foundation of modern microservices observability, but getting it right in production requires more than just enabling auto-instrumentation. This guide covers production-tested OpenTelemetry best practices that help engineering teams achieve reliable distributed tracing, control observability costs, and extract maximum value from their telemetry data.