Search arXiv⌕ Search

arXiv · 2610.34620

Built to Remember, Shipped to Forget: Operational Contracts, Failures, and Repairs in Containerized LLM Agent Deployments

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.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Meriem Mastouri, Emna Ksontini, Amine Barrak. 2026-10-25. Built to Remember, Shipped to Forget: Operational Contracts, Failures, and Repairs in Containerized LLM Agent Deployments. https://arxiv.org/abs/2610.34620

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

How Quickly Do Development Teams Update Their Vulnerable Dependencies?

Industry practitioners are increasingly concerned with software that contains vulnerable versions of third-party dependencies that are included both directly and transitively. To address this problem, projects are encouraged to both (a)~quickly update to non-vulnerable versions of dependencies and (b)~be mindful of the update practices of the dependencies they choose to use. To this end, researchers have proposed metrics to measure the responsiveness of the development teams of the packages in keeping their dependencies updated: Mean-Time-To-Update (MTTU) and Mean-Time-To-Remediate (MTTR). While MTTU covers all dependencies, MTTR quantifies the time needed for a package to update its vulnerable dependencies. However, existing metrics fail to capture important nuances, such as considering floating versions and prioritizing recent updates, leading to inaccurate reflections of a development team's update practices. \textit{The goal of this study is to aid practitioners in understanding how quickly packages update their dependencies.} We propose two novel metrics, Mean-Time-To-Update for dependencies (MTTU) and Mean-Time-To-Remediate for vulnerable dependencies (MTTR), that overcome the limitations of existing metrics. We conduct an empirical study using $163,207$ packages in npm ($117,129$), PyPI ($42,777$), and Cargo ($3,301$) and characterize how the ecosystems differ in MTTU and MTTR, as well as what package characteristics influence MTTU and MTTR. We found that most packages have a relatively fast dependency update practice. We further study whether MTTU can be used as a proxy for MTTR when sufficient vulnerability data is not available. As we did not find enough statistical evidence for a strong proxy, our findings suggest that MTTU could only be partially used (may be used but with caution) as a proxy for MTTR when vulnerability data is not available.

cs.SE↗

Human-AI Synergy in Agentic Code Review

Code review is a critical software engineering practice where developers review code changes before integration to ensure code quality, detect defects, and improve maintainability. In recent years, AI agents that can understand code context, plan review actions, and interact with development environments have been increasingly integrated into the code review process. However, there is limited empirical evidence to compare the effectiveness of AI agents and human reviewers in collaborative workflows. To address this gap, we conduct a large-scale empirical analysis of 278,790 code review conversations across 300 open-source GitHub projects. In our study, we aim to compare the feedback differences provided by human reviewers and AI agents. We investigate human-AI collaboration patterns in review conversations to understand how interaction shapes review outcomes. Moreover, we analyze the adoption of code suggestions provided by human reviewers and AI agents into the codebase and how adopted suggestions change code quality. We find that human reviewers provide additional feedback than AI agents, including understanding, testing, and knowledge transfer. Human reviewers exchange 11.8% more rounds when reviewing AI-generated code than human-written code. Moreover, code suggestions made by AI agents are adopted into the codebase at a significantly lower rate than suggestions proposed by human reviewers. Over half of unadopted suggestions from AI agents are either incorrect or addressed through alternative fixes by developers. When adopted, suggestions provided by AI agents produce significantly larger increases in code complexity and code size than suggestions provided by human reviewers. Our findings suggest that while AI agents can scale defect screening, human oversight remains critical for ensuring suggestion quality and providing contextual feedback that AI agents lack.

cs.SE↗

Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs

Training tool-calling agents requires large-scale trajectory data with verifiable labels, yet existing approaches either synthesize environments that diverge from real API behavior or generate tasks without ground-truth outcomes for verification. We present FireFly, a pipeline for generating verified tool-call data from real-world MCP servers. Our key insight is to invert the standard synthesis pipeline: rather than generating tasks and hoping they are solvable, we first let a strong LLM explore real APIs along graph-guided DAG structures, then synthesize tasks backward from observed outcomes, guaranteeing label correctness by construction. To handle the scale of real-world tool spaces (${\sim}$1,000 tools), we build a pairwise tool graph and sample sub-DAGs to focus exploration on semantically coherent workflows. To address environment drift in live APIs, we construct a retrieval-augmented simulator that caches all exploration results and replays them during training and evaluation, enabling fully offline and reproducible RL. Applying this pipeline yields 5,144 verified tasks spanning 240 servers and 993 tools. A 4B-parameter model trained with GRPO on FireFly matches Claude Sonnet 4.6 on our held-out test set and shows improvements on multiple tool-calling benchmarks including Tau2-Bench, MCPMark, and MCP-Atlas.

cs.SE↗