Perry Correll examines the shift to agentic AI-driven telemetry, explaining how combining machine and human context enables intelligent observability and self-healing IT systems.

The Agentic AI-Driven Future of Telemetry

Author: Perry Correll

For years, telemetry—the stream of metrics, events, logs, and traces (MELT)—has supported IT and security teams in monitoring and responding to issues. Traditionally, telemetry acted as a passive data source, showing what happened but rarely why or how to respond.

The Rise of Agentic AI for Telemetry

With the emergence of agentic AI—systems where autonomous agents analyze and act upon data—the nature of telemetry is transforming. No longer just operational exhaust for dashboards, telemetry becomes “fuel” for AI agents. These agents are empowered to:

  • Triaging alerts
  • Orchestrating incident response workflows
  • Remediating problems in real time

Agentic telemetry actively fuses machine signals—like logs and metrics—with human-generated context, such as:

  • Support tickets
  • Wikis and runbooks
  • Slack or chat conversations
  • CI/CD workflow metadata

This union allows AI agents to provide enriched, explainable incident analysis and suggested remediations.

Human-in-the-Loop Remains Key

Despite this automation, maintaining humans in the decision loop ensures accountability and trust. Operators review AI suggestions, supply feedback, and make high-judgment calls, preserving accuracy and ethical integrity.

Shortcomings of Traditional Telemetry

The article highlights several challenges with legacy telemetry approaches:

  • Context gap: Logs and metrics lack human and operational context, slowing investigations and causing analyst burnout.
  • Growth pressure: Telemetry data proliferates faster than budgets, forcing costly scaling or data sacrifice, and risking blind spots.
  • Legacy architectures: Older SIEMs and log analysis tools weren’t built for the scale and query patterns required by machine agents.

Solution: AI-First Telemetry Architecture

To address these, the proposed architecture emphasizes:

  • Structuring and normalizing data at ingest (versus brittle downstream parsing)
  • Schema-agnostic, federated storage compatible with formats like OTLP, OCSF, and ECS
  • Lakehouse-style storage for high-speed, high-volume AI-driven queries
  • Fusing machine and human context for actionable insight

The architecture supports both agentic and manual investigations, reducing costs and speeding up response time.

Benefits Achieved

  • Unlimited scalability: Streamlined storage architecture allows thousands of agent queries without overload
  • Lower ingestion costs: Query data in place, decoupled from expensive data movement
  • Operator empowerment: Engineers focus on high-value, complex problem solving, while agents handle routine triage and enrichment
  • Faster investigations: AI agents rapidly correlate alerts, documentation, and past incidents, handing contextualized actions to humans

Future-Ready, Open Standards

Agentic telemetry is designed for adaptability:

  • Multiple supported data schemas
  • Interoperability with diverse data stores
  • Compatibility with evolving AI agents
  • Openness to avoid vendor lock-in

Conclusion

In the AI era, telemetry will no longer be just a passive byproduct—it’s evolving into actionable fuel for intelligent agents. By re-architecting telemetry with structured ingestion, schema flexibility, and human context fusion, organizations can boost their investigation speed, reduce costs, and empower operators. This architecture paves the way for resilient, self-healing IT and security operations.

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