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We are witnessing the fastest expansion of the software development lifecycle in history. Generative AI tools have turned every developer into a hyper-productive builder, and in some cases, turned non-technical team members into creators of production-bound services. But this speed comes with a hidden cost. When the volume of code grows exponentially, the surface area for failure grows with it. The real challenge of modern software engineering is not Day 1 code generation; it is Day 2 operations.

Starting an experiment with an LLM has never been easier. Keeping a growing collection of those experiments consistent is another matter. Earlier this year, as more teams began exploring AI features here at Grafana Labs, we repeatedly encountered the same pattern: a new experiment would start, move quickly, and build its own client for whichever model provider it needed. The next experiment would do the same, with a slightly different abstraction for streaming, tools, errors, or provider configuration.

There are many conversations about the use of AI, particularly how engineering teams are using it in their coding workflows. Manual work is being replaced by agent-driven automation, and human value increasingly lies in the higher-order work: writing specs, thinking through architecture, steering the direction, exercising taste, and reviewing the output.