Show Notes
Advanced software systems have long been more complex than any single engineer can fully understand. Observability is the established solution to this problem, but with AI agents now generating code, deploying changes, and operating autonomously, the challenge of understanding large software systems is entering a new dimension.
Grafana is an open source observability platform, and one of the most widely used in the world. The company builds tools that help teams collect, visualize, and act on telemetry data across logs, metrics, and traces. They are now extending that capability into the agentic era with AI-powered investigation and monitoring tools.
Anthony Woods is a co-founder of Grafana Labs. In this episode, he joins Matt Merrill to discuss how AI-generated code is straining software operations, why telemetry data volume has become as much a problem as a solution, how Grafana is adapting to a world where agents are the primary consumers of observability data, and what keeps him up at night about where the industry is headed.
Sponsorship inquiries: [email protected]
The post Grafana’s Approach to AI-Native Observability appeared first on Software Engineering Daily.
More from Software Engineering Daily
Codebases in enterprise organizations often span hundreds of thousands, or even millions, of lines of code. In many cases, changes must follow established patterns precisely, or risk introducing costly security or operational issues. A common challenge with AI coding tools is that they struggle at this scale: LLM context windows cover only a fraction of a large codebase, and output quality degrades the longer an agent runs.
In recent years, the main impact of AI has been on the digital world, and most notably software engineering. However, its longer-term effect on the physical world may prove even more profound, reshaping how cars drive themselves, how factories run, and how robots move through homes and warehouses.
Software testing is widely understood as essential to building reliable systems, and it’s one of the hardest things to get right. A fundamental issue is that tests are good at confirming the behavior a team expects, but the most damaging failures tend to hide in the edge cases no one thought to check during development. Those blind spots are only growing as AI produces more code that no human has closely reviewed.
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer break down the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry.
The popular story of Steve Jobs is that he co-founded Apple, was pushed out, wandered through a failed venture called NeXT, then returned to save the company and usher in the iPod and iPhone. That version is memorable, widely repeated, and, in an important sense, wrong. His years at NeXT are usually treated as a footnote, when they were among the most consequential of his life and career.