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Built to Remember, Shipped to Forget: Operational Contracts, Failures, and Repairs in Containerized LLM Agent Deployments
arXiv cs.SE0 views
arXiv:2610.34620v1 Announce Type: new Abstract: LLM agent systems reach production as container images run by platforms such as Docker, Compose, and Kubernetes. Framework and platform document opposite expectations: to the platform a container is disposable, while the framework persists conversations, pauses runs for human input, and resumes interrupted work from saved state; each side specifies only its own behavior, so the obligations their composition creates are stated nowhere. We present the first empirical study of this boundary, stating these obligations as operational contracts: guarantees a deployment must provide to preserve an agent system's required behavior when a deployment-platform event occurs. From the first-party documentation of LangGraph and the three platforms we derive 14 contracts and evaluate them with three independent sources of evidence: 433 deployed open-source agent systems, seven events induced on 12 of them, and 122 developer repairs mined from 39,261 commits, pull requests, and issues. At least one contract applies to 75.5% of the systems, yet its guarantee is routinely missing: of systems that must keep state beyond the process, 37.4% lose it to a restart and 41.5% to a container replacement, and every assessable deployment stops the framework's production server mid-drain. Induced events turn 14 of 16 predicted violations into runtime failures and all seven predicted holds pass. The failures are born in deployment but repaired in code: developer repairs independently cover twelve of the fourteen contracts, 78% change application code, and only two in three fully restore the violated guarantee. The 14-contract catalogue, detection rules, fault-injection harness, and 35-pattern repair taxonomy provide a basis for checking and strengthening agent deployments.
Source:https://arxiv.org/abs/2610.34620PreviousDo AI Coding Agents Write Infrastructure as Code Like Humans? An Empirical Study of GitHub Pull RequestsNextCodeMeepo: A Multi-Agent Workflow for Repository-Level Code Migration and Optimization
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