Your Data is Made Powerful By Context (so stop destroying it already) (xpost)

2026-03-09 · Source: charity.wtf - Charity.wtf · Depth: Unknown, medium

Summary

The article critiques the prevalent "three pillars" model for observability data collection, arguing it catastrophically destroys crucial contextual relationships between data attributes, making data less powerful. It asserts that data's value increases combinatorially, not linearly, with added context; for instance, 8 fields offer 255 possible combinations, while 50 fields yield 1.1 quadrillion. This loss of relational seams hinders effective software engineering use cases and agentic validation. AI-SRE tools reportedly struggle with siloed data, often seeking raw, context-rich telemetry instead. The author emphasizes that AI agents, essential for validating rapid, high-volume changes in production, demand speed, flexibility, extensive context, and precision tooling, which the current fragmented data approach fails to deliver.

Key takeaway

For software engineering teams designing observability systems, recognize that the traditional "three pillars" model actively degrades data value by destroying context. You should prioritize collecting wide, context-rich telemetry with all relational attributes intact to enable effective AI agent validation and precise anomaly detection. This approach is critical for managing the exponentially higher change rates and validation needs of agentic development workflows.

Key insights

The "three pillars" observability model destroys data context, hindering AI agent validation and making data exponentially less powerful.

Principles

• Data power grows combinatorially with context.
• Relational seams are the most valuable part of data.
• AI agents require rich, contextual telemetry for validation.

In practice

• Avoid siloed data collection for observability.
• Prioritize capturing all contextual attributes in logs.
• Design systems for high-cardinality data handling.

Topics

• Observability
• Data Context
• AI Agents
• Telemetry
• Software Engineering
• Anomaly Detection

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Editorial summary, takeaway, and curation by AIssential. Original article published by charity.wtf - Charity.wtf.