Summary

Charity argues that the root cause of observability failures isn't culture or tooling but a fundamental data architecture problem: the 'three pillars' model (metrics, logs, traces) destroys the relational context that makes telemetry data exponentially more powerful. As agentic AI workflows demand increasingly precise production validation, fragmented telemetry siloes become not just suboptimal but a critical bottleneck — AI agents are already abandoning three-pillars data in favor of richer, context-intact signals.

Key Insight

The three pillars observability model is not just inefficient but actively destructive — it eliminates context at write time, and no amount of AI-powered joining can restore what was never preserved, which makes it incompatible with the precision demands of agentic software development.

Spicy Quotes (click to share)

• 3

  Data is made powerful by context. The more context you collect, the more powerful it becomes.

• 7

  By spinning your telemetry out into siloes based on signal type, the three pillars model ends up destroying the most valuable part of your data: its relational seams.

• 5

  Joins across data siloes can be better than nothing, yes. But they don't restore the relational seams.

• 6

  Our wisdom must be encoded into the system, or it does not exist.

• 8

  In this situation, as in so many others, AI is both the sickness and the cure.

Tone

urgent and persuasive, with dry humor