Telemetry-Native Reliability Engineering for Agentic AI Systems: Topology-Aware Failure Correlation and Decision Traceability

Shivam Zia Dube; Rajat Kumar Varshney; Sanjay P Kumar

Telemetry-Native Reliability Engineering for Agentic AI Systems: Topology-Aware Failure Correlation and Decision Traceability

Author(s)	Mr. Shivam Zia Dube, Mr. Rajat Kumar Varshney, Mr. Sanjay P Kumar
Country	India
Abstract	Agentic artificial intelligence systems combine large language models, tool-calling runtimes, retrieval services, application programming interfaces, policy guardrails, memory stores, and human approval paths. These systems create a reliability problem that differs from conventional microservices because failures may arise from model drift, tool misuse, dependency latency, invalid action plans, retrieval errors, policy denials, or release changes across the agent execution graph. This paper proposes Telemetry-Native Reliability with Decision Traceability (TNR-DT), a framework that combines OpenTelemetry-aligned signal normalization, topology-aware incident correlation, predictive change-risk scoring, deterministic policy routing, rollback-readiness checks, and decision-trace evidence generation. The study evaluates the framework through a benchmark-referenced reproducible simulation. It uses current public benchmark references, including ITBench, ITBench Trajectories, AIOps2025/RCA100, AgentOps-Bench, OpenTelemetry Demo Dataset, and GH Archive, to define operational scenarios, telemetry classes, fault categories, and change-event features. A fixed-seed AgentOps replay then evaluates threshold-local alerting, global time-window grouping, and TNR-DT across 100 seeds and 240 incident episodes per seed. TNR-DT reduced non-actionable alerts per incident from 8.69 to 3.11, reduced modeled mean time to restore from 101.81 to 54.23 minutes, improved top-three root-cause hit rate from 55.07% to 81.30%, increased evidence completeness from 53.00% to 96.08%, and reduced policy-exception escape rate from 11.30% to 2.57%. Change-risk prediction improved only modestly, with area under the curve increasing from 0.620 to 0.653. The stronger contribution is therefore not predictive superiority alone, but a traceable reliability operating model that links agent telemetry, dependency topology, policy decisions, rollback evidence, and incident reconstruction.
Keywords	Agentic AI, AIOps, Telemetry Correlation, Reliability Engineering, Decision Traceability, Policy-as-Code.
Field	Computer Applications
Published In	Volume 17, Issue 3, July-September 2026
Published On	2026-09-09

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Telemetry-Native Reliability Engineering for Agentic AI Systems: Topology-Aware Failure Correlation and Decision Traceability

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