The death of the dashboard: why agentic AI is choking on legacy observability tools

Observability, traditionally tailored for human operators, is becoming obsolete as AI agents take over tasks such as issue detection and resolution. These agents require a different type of data architecture that current systems, designed to prevent human cognitive overload and manage storage costs, do not provide. The existing telemetry data, optimized through practices like sampling and aggregation, often lacks the granularity and context necessary for AI to function effectively, leading to inefficiencies and increased risks. As AI agents rapidly ship more code, they inadvertently introduce more bugs and security vulnerabilities, with research indicating an increase in incidents and defects. The core issue lies in the architectural design of observability data, which fails to support the context and depth required by AI agents. To truly leverage AI potential, organizations must shift their focus from merely providing AI access to existing telemetry to redesigning data collection and correlation strategies that align with AI agents' needs.

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