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# Beyond correlation to autonomous action: Why “good enough” observability fails in the age of agentic AI

**[Dynatrace](https://daily.dev/sources/dynatrace)** · 6 min read · 0 upvotes · 0 comments

## Summary

Traditional observability platforms built around probabilistic AI and correlation-based insights are insufficient for agentic AI systems that must act autonomously. The core argument is that AI agents require deterministic, context-rich, unified telemetry rather than probabilistic guesses, because autonomous systems acting on business-critical infrastructure cannot tolerate approximations or require human validation loops. The post distinguishes between probabilistic AI (correlating signals to propose likely root causes) and deterministic AI (causal analysis that pinpoints exact root cause and business impact). It argues observability must evolve from a human-facing dashboard tool into a control plane embedded within autonomous execution, with telemetry optimized at ingest, real-time dependency mapping, and unified data architecture — enabling AI agents to remediate, prevent, and optimize without waiting for human interpretation.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.dynatrace.com/news/blog/beyond-correlation-to-autonomous-action>

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Tags: [#devops](https://daily.dev/tags/devops), [#ai-agents](https://daily.dev/tags/ai-agents), [#observability](https://daily.dev/tags/observability), [#agentic-ai](https://daily.dev/tags/agentic-ai)

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