# Agent-Native Telemetry: Verifiable State-Delta Evidence for Autonomous Operations

arXiv: https://arxiv.org/abs/2608.16178 · HTML: https://mlsanity.com/paper/2608.16178/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 17 Aug 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 10 of 10)

- [AIOpsLab: A Holistic Framework to Evaluate AI Agents for Enabling Autonomous Clouds](https://mlsanity.com/paper/2501.06706/) (2025)
- [Cloud-OpsBench: A Reproducible Benchmark for Agentic Root Cause Analysis in Cloud Systems](https://mlsanity.com/paper/2603.00468/) (2026)
- [LogEval: A Comprehensive Benchmark Suite for Large Language Models In Log Analysis](https://mlsanity.com/paper/2407.01896/) (2024)
- [AgentTrace: A Structured Logging Framework for Agent System Observability](https://mlsanity.com/paper/2602.10133/) (2026)
- [LogShrink: Effective Log Compression by Leveraging Commonality and Variability of Log Data](https://mlsanity.com/paper/2309.09479/) (2023)
- [OpenRCA 2.0: From Outcome Labels to Causal Process Supervision](https://mlsanity.com/paper/2606.27154/) (2026)
- [The Benefit of Hindsight: Tracing Edge-Cases in Distributed Systems](https://mlsanity.com/paper/2202.05769/) (2022)
- [Unlocking the Power of Numbers: Log Compression via Numeric Token Parsing](https://mlsanity.com/paper/2408.05760/) (2024)
- [When AIOps Become "AI Oops": Subverting LLM-driven IT Operations via Telemetry Manipulation](https://mlsanity.com/paper/2508.06394/) (2025)
- [HYVE: Hybrid Views for LLM Context Engineering over Machine Data](https://mlsanity.com/paper/2604.05400/) (2026)

## Abstract (arXiv)

Operational telemetry is predominantly engineered for human reading: systems repeatedly serialize verbose prose, static keys, and redundant context across billions of log lines. As autonomous AI agents become primary operational consumers, feeding them traditional logs wastes scarce context capacity parsing lexical syntax rather than reasoning over system state changes -- all while lacking cryptographic guarantees of provenance or collection completeness. This paper introduces agent-native telemetry, an operational evidence architecture for autonomous machine operators founded on verifiable state deltas rather than human prose. We present the Agent Telemetry Protocol (ATP) and the State-Delta Evidence Ledger, an implementation that structures operational facts into four core evidence primitives (Transitions, Observations, Relations, and State Checkpoints) governed by content-addressed schemas, while isolating uncurated text as digest-verified opaque references. Producers sign and hash-chain batches for atomic collector append. Verified records feed two parallel agent access paths: a stateless protocol decoder emitting compact positional rows, and a stateful semantic gateway serving bounded graph capsules. We prove an information-preservation lower bound and formalize a ledger-relative verified negative theorem for provable event non-occurrence. On distributed microservice benchmarks (AIOpsLab and OpenTelemetry Astronomy Shop), ATP reduces raw wire payload and modeled cloud query scan costs by 96.4% relative to OpenTelemetry JSON, reduces LLM context tokens by 88.8% and query operations by 66.2%, detects all 500 tested adversarial storage mutations, and yields zero successful prompt injections across 50 adversarial trials per ATP configuration.