#agent-testing News & Analysis

5 articles tagged with #agent-testing. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AIBullisharXiv – CS AI · Jun 117/10

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Agents All the Way Down; A Methodology for Building Custom AI Agents from Substrate to Production

Researchers present 'Agents All the Way Down,' a framework-agnostic methodology for building custom AI agents from development through production. The approach combines preconditions (substrate setup and building blocks) with three iterative practices (prototyping, CLI deployment via the Turtle pattern, and agent-driven testing), offering developers a structured path to create specialized agents tailored to specific applications rather than relying on general-purpose models.

AIBearisharXiv – CS AI · May 127/10

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Log analysis is necessary for credible evaluation of AI agents

Researchers argue that AI agent benchmarks relying solely on pass/fail outcomes mask critical evaluation gaps, including inflated scores from shortcuts, poor real-world predictability, and hidden dangerous behaviors. Log analysis—systematic tracking of agent inputs, execution, and outputs—is proposed as essential for credible evaluation, with case studies showing performance metrics can underestimate capability by 50% and hide deployment failure modes.

AINeutralarXiv – CS AI · Jun 116/10

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Layer-Isolated Evaluation: Gating the Deterministic Scaffold of a Production LLM Agent with a No-LLM, Regression-Locked Test Harness

Researchers present layer-isolated evaluation, a deterministic testing framework that decomposes LLM agents into eight functional layers, each validated independently without requiring LLM execution. Testing across 238 cases reveals that aggregate end-to-end metrics mask localized regressions, with targeted layer failures causing 25-91 percentage point drops in component-specific tests while barely affecting overall pass rates.

AINeutralarXiv – CS AI · Jun 96/10

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OmniGameArena: A Unified UE5 Benchmark for VLM Game Agents with Improvement Dynamics

Researchers introduce OmniGameArena, a comprehensive UE5-based benchmark for evaluating vision-language model agents across diverse game environments (solo, PvP, cooperative), along with the Improvement Dynamics Curve methodology that tracks agent performance evolution through iterative refinement rather than single snapshots.

AINeutralarXiv – CS AI · May 296/10

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MINDGAMES: A Live Arena for Evaluating Social and Strategic Reasoning in Multi-Agent LLMs

Researchers introduced Mindgames, a multi-game arena platform for evaluating large language model agents' social and strategic reasoning across four game environments. A 2025 competition cycle tested 944 agents from 76 teams, revealing that top-performing LLMs rely heavily on explicit structural scaffolding and struggle with rule adherence, while some game environments conflate robustness to errors with genuine strategic ability.