The article introduces the Consistency Quadrant, a visual framework that maps structural variance against execution outputs to gauge the reliability of large language model (LLM) coding agents without requiring ground truth. It explains how this approach can estimate agent reliability by analyzing patterns in the model’s behavior across different inputs and outputs. The post was published on Towards Data Science.
arXiv:2607. 09510v1 Announce Type: cross Abstract: Large language model (LLM) coding agents are increasingly deployed to autonomously perform software engineering tasks in terminal-based environments, making their reliability a growing concern.
arXiv:2607. 13071v1 Announce Type: cross Abstract: Agentic LLM coding tools compress long session histories into compaction summaries that subsequent sessions inherit as ground truth.
Answer accuracy is an insufficient reliability signal for LLM data agents. In structured-data tasks, a benchmark-correct answer can be produced by an invalid trace. This paper introduces Trace Integri...
arXiv:2610.16661v1 Announce Type: new Abstract: An LLM agent using tools can fail silently. It stops before the task is done, raises no error, and answers fluently. Two kinds of monitor address this,...
arXiv:2608. 13867v1 Announce Type: cross Abstract: AI coding agents are commonly evaluated as models but deployed as systems.
Enterprise practitioners read agent leaderboards as if they ranked agent capability. We show, across three open agent-trace benchmarks (TheAgentCompany, $τ^2$-bench, and AppWorld), that the agent main effect accounts for less than 3% of total variance in every dataset and check type, while the agent-by-task interaction accounts for 7-23%.
arXiv:2608. 04439v1 Announce Type: cross Abstract: Large language models (LLMs) have made notable progress in code generation, but they still struggle on challenging tasks that require sophisticated algorithms or complex implementations.
arXiv:2602. 16666v3 Announce Type: replace Abstract: AI agents are increasingly deployed to execute important tasks.
arXiv:2610.16067v1 Announce Type: cross Abstract: Agent evaluation harnesses are software systems that run test suites, generate inputs, and decide outcomes with test oracles. This paper audits such...
Teams increasingly build software from cooperating large language model (LLM) agents, often assuming more agents mean more reliability. The paper proposes a framework that combines hierarchical imprecise-probability assessment of single LLMs with a Bayesian network of system failure logic to produce a reliability envelope. Experiments on a pipeline extracting regulated chemical-hazard statements show that majority voting over three models protects against the weakest model but does not clearly outperform the best, and a voter failing on 5% of demands largely erases its advantage.
The paper argues that answer accuracy alone is insufficient for evaluating large language model (LLM) data agents, especially in structured-data tasks where a correct answer can be produced by an invalid trace. It introduces Trace Integrity as a reliability criterion that ensures the computation behind an answer is explicit, executable, schema-valid, operator-faithful, replayable, answer-consistent, and auditable. The authors operationalize this concept with execution contracts and present the CAIT (Correct Answer / Invalid Trace) Rate to quantify how often answer-only evaluations mistakenly reward unsupported outputs, demonstrating that accuracy, trace validity, and silent-failure risk are distinct signals.
The paper "Reliability Engineering for AI Systems: Challenges, Methods, and Directions" discusses how AI reliability extends beyond correct outputs to include consistent operation of retrieval, memory, tool use, permissions, human oversight, and inter-system interactions, especially for generative AI. It adapts traditional reliability engineering techniques—such as failure definitions, operational envelopes, FMEA, accelerated testing, field monitoring, and reliability growth—to AI, proposing a four-level diagnostic framework and a TEVV (Test, Evaluation, Verification, Validation) approach. The authors also introduce the SMART framework for statistical guidance and reference NIST’s AI risk management framework, illustrating their concepts with three case studies and emphasizing the urgent need for new measurements and safety guardrails as AI systems evolve rapidly.