Agent Mesh: Reliability Primitives for Non-Idempotent Agent Delegation - Identity Adequacy and Evidence Adequacy
Not on the map. Why?
Abstract (arXiv)
Autonomous agents increasingly perform bounded software tasks under an orchestrator that retries, resumes, and budgets them. The machinery such orchestrators reach for is the service mesh's: retry, timeout, and error-rate circuit breaking. We report a failure study of a production agentic software-delivery platform over 147 numbered incidents spanning 81 runs, each with a measured cost and, in most cases, a mutation proof reproducing the failure. All three assumptions those primitives rest on are violated in practice, and we quantify the consequences: a loop of fifty-four consecutive successful tool calls no error-rate breaker could see; a progress signal constant by construction, guaranteeing a false trip on the third repair round and driving one run from six of six components to three; twenty-one events accumulated across six invocations of one delegation, making a correct, idempotent component unwinnable; a misrouted failure that woke five components for a two-component fault, leaving three bystanders regressing working code; and twelve incidents in which the enforcement layer blocked correct work, the most expensive costing 107 agent turns and zero accepted writes. We find one cross-cutting cause and its dual. Identity adequacy: in five separate subsystems an identity that failed to discriminate produced a confident wrong answer, and two of them derived the corrective rule independently. Evidence adequacy: a reliability decision may be taken only on evidence capable of moving, attributable to what it measures, and deterministic under identical conditions. From the findings we derive seven reliability primitives whose enforcement unit is the delegation rather than the message, and specify the controlled evaluation the study motivates but does not constitute.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 26 references that are in the map. First posted on arXiv 26 Aug 2026.
Builds on
10 of 26 references.
References · 10
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
- 2023AgentBench: Evaluating LLMs as Agentscited by 1,211
- 2025Why Do Multi-Agent LLM Systems Fail?cited by 559
- 2025Defeating Prompt Injections by Designcited by 256
- 2025Survey on Evaluation of LLM-based Agentscited by 190
- 2025Securing AI Agents with Information-Flow Controlcited by 108
- 2015Lightweight Asynchronous Snapshots for Distributed Dataflowscited by 1
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026ClayBuddy: A Framework, Evaluation, & Mitigation of Coding Agent Failures
- 2026When Errors Become Narratives: A Longitudinal Taxonomy of Silent Failures in a Production LLM Agent Runtime
- 2026When Agentic Executions Fail: Detecting and Localizing Runtime Faults from Telemetry
- 2026Incident-Arena: Getting agents to the last nine of reliability
- 2026ReliabilityBench: Evaluating LLM Agent Reliability Under Production-Like Stress Conditions
- 2026OrchestraBench: Evaluating Multi-Agent Orchestration Failure Modes, Recovery, and Decomposition Quality
- 2026AgentEval: DAG-Structured Step-Level Evaluation for Agentic Workflows with Error Propagation Tracking
- 2026When Agents Fail to Act: A Diagnostic Framework for Tool Invocation Reliability in Multi-Agent LLM Systems