What building AI features taught us about the future of observability

Axiom recently conducted an innovative "AI week," where the entire company was divided into small teams to rapidly develop AI features, resulting in three production-ready tools: natural language querying, dashboard generation, and smart query naming. This experiment emphasized the importance of understanding when AI works effectively, rather than simply making it function, by prioritizing evaluation and context-specific insights. Teams learned to validate AI capabilities manually before automating processes and developed frameworks for systematic evaluation to avoid "vibe coding." The initiative highlighted the challenges of AI observability, context management, and error compounding in workflows, underscoring the need for infrastructure that evolves with AI advancements. As Axiom launches these features, they invite feedback from AI builders to further refine their offerings, while ensuring data privacy through partnerships with trusted enterprise providers.

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