O'Reilly Report - Context Engineering for Observability
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O'Reilly Report - Context Engineering for Observability
Context engineering is the discipline that makes observability usable. By embedding meaning directly into telemetry, it turns raw signals into decision-ready insight for both humans and agents—helping teams move beyond dashboards toward faster reasoning, clearer context, and more trustworthy automation.
In Context Engineering for Observability, O’Reilly explores:
- How AI increases telemetry volume, driving more complexity and cost
- How active telemetry adapts signals to the needs of the consumer—human or agent
- Why observability needs built-in context to make telemetry actionable, not just available
Context engineering is the discipline that makes observability usable. By embedding meaning directly into telemetry, it turns raw signals into decision-ready insight for both humans and agents—helping teams move beyond dashboards toward faster reasoning, clearer context, and more trustworthy automation.
In Context Engineering for Observability, O’Reilly explores:
- How AI increases telemetry volume, driving more complexity and cost
- How active telemetry adapts signals to the needs of the consumer—human or agent
- Why observability needs built-in context to make telemetry actionable, not just available
Context engineering is the discipline that makes observability usable. By embedding meaning directly into telemetry, it turns raw signals into decision-ready insight for both humans and agents—helping teams move beyond dashboards toward faster reasoning, clearer context, and more trustworthy automation.
In Context Engineering for Observability, O’Reilly explores:
- How AI increases telemetry volume, driving more complexity and cost
- How active telemetry adapts signals to the needs of the consumer—human or agent
- Why observability needs built-in context to make telemetry actionable, not just available
Context engineering is the discipline that makes observability usable. By embedding meaning directly into telemetry, it turns raw signals into decision-ready insight for both humans and agents—helping teams move beyond dashboards toward faster reasoning, clearer context, and more trustworthy automation.
In Context Engineering for Observability, O’Reilly explores:
- How AI increases telemetry volume, driving more complexity and cost
- How active telemetry adapts signals to the needs of the consumer—human or agent
- Why observability needs built-in context to make telemetry actionable, not just available
