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Monitoring Agentic Systems Before They're Reliable
Authors:
Marisa Ferrara Boston,
Glen Hanson,
Effi Georgala,
JD Hudgens,
Heather Frase
Abstract:
Agentic systems entering production typically operate as partially integrated assemblies where structural defects, not task-level errors, dominate the failure landscape. At this maturity level, task-level error detection may be infeasible: structural failure modes mask the signal that task-level monitors are designed to detect.We present a monitoring and triage methodology that decomposes agentic…
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Agentic systems entering production typically operate as partially integrated assemblies where structural defects, not task-level errors, dominate the failure landscape. At this maturity level, task-level error detection may be infeasible: structural failure modes mask the signal that task-level monitors are designed to detect.We present a monitoring and triage methodology that decomposes agentic system evaluation into three dimensions (quality, suitability, efficiency) at three monitoring scopes (within-run, cross-run, structural), using variance as a characterization signal. Findings are routed through severity classification adapted from FMEA, concentrating human attention on the subset that warrants investigation. We evaluate on a synthetic testbed of 220 runs across 120 document bundles with controlled error injection.Three results emerge. Monitor scope determines failure type: within-run monitors surface deterministic stage defects (CV = 0.02), cross-run monitors surface stochastic integration consequences (CV = 1.25, 24% at L2), and a structural monitor identifies an integration gap with perfect consistency (CV = 0.00). Injected task-level errors are indistinguishable from clean baselines, confirming structural defects mask task-level signal. Deterministic triage routes 97% of findings to automated tracking, leaving the 2% reflecting variable behavior for human investigation.We propose, on Stage 1 evidence, a maturity-staging model in which monitoring transitions from structural characterization to error detection to reliability tracking as integration defects resolve. The taxonomy, CV-based scope characterization, and severity model transfer architecturally to document-driven, multi-stage agentic workflows in regulated industries; specific calibrations are domain-specific. Deploy monitoring early: the first thing it finds is the most important thing to fix.
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Submitted 1 June, 2026;
originally announced June 2026.
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Red Teaming for Generative AI, Report on a Copyright-Focused Exercise Completed in an Academic Medical Center
Authors:
James Wen,
Sahil Nalawade,
Zhiwei Liang,
Catherine Bielick,
Marisa Ferrara Boston,
Alexander Chowdhury,
Adele Collin,
Luigi De Angelis,
Jacob Ellen,
Heather Frase,
Rodrigo R. Gameiro,
Juan Manuel Gutierrez,
Pooja Kadam,
Murat Keceli,
Srikanth Krishnamurthy,
Anne Kwok,
Yanan Lance Lu,
Heather Mattie,
Liam G. McCoy,
Katherine Miller,
Allison C. Morgan,
Marlene Louisa Moerig,
Trang Nguyen,
Alexander Owen-Post,
Alex D. Ruiz
, et al. (16 additional authors not shown)
Abstract:
Background: Generative artificial intelligence (AI) deployment in academic medical settings raises copyright compliance concerns. Dana-Farber Cancer Institute implemented GPT4DFCI, an internal generative AI tool utilizing OpenAI models, that is approved for enterprise use in research and operations. Given (1) the exceptionally broad adoption of the tool in our organization, (2) our research missio…
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Background: Generative artificial intelligence (AI) deployment in academic medical settings raises copyright compliance concerns. Dana-Farber Cancer Institute implemented GPT4DFCI, an internal generative AI tool utilizing OpenAI models, that is approved for enterprise use in research and operations. Given (1) the exceptionally broad adoption of the tool in our organization, (2) our research mission, and (3) the shared responsibility model required to benefit from Customer Copyright Commitment in Azure OpenAI Service products, we deemed rigorous copyright compliance testing necessary.
Case Description: We conducted a structured red teaming exercise in Nov. 2024, with 42 participants from academic, industry, and government institutions. Four teams attempted to extract copyrighted content from GPT4DFCI across four domains: literary works, news articles, scientific publications, and access-restricted clinical notes. Teams successfully extracted verbatim book dedications and near-exact passages through various strategies. News article extraction failed despite jailbreak attempts. Scientific article reproduction yielded only high-level summaries. Clinical note testing revealed appropriate privacy safeguards.
Discussion: The successful extraction of literary content indicates potential copyrighted material presence in training data, necessitating inference-time filtering. Differential success rates across content types suggest varying protective mechanisms. The event led to implementation of a copyright-specific meta-prompt in GPT4DFCI; this mitigation has been in production since Jan. 2025.
Conclusion: Systematic red teaming revealed specific vulnerabilities in generative AI copyright compliance, leading to concrete mitigation strategies. Academic medical institutions deploying generative AI should implement continuous testing protocols to ensure legal and ethical compliance.
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Submitted 2 July, 2025; v1 submitted 26 June, 2025;
originally announced June 2025.
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AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Authors:
Shaona Ghosh,
Heather Frase,
Adina Williams,
Sarah Luger,
Paul Röttger,
Fazl Barez,
Sean McGregor,
Kenneth Fricklas,
Mala Kumar,
Quentin Feuillade--Montixi,
Kurt Bollacker,
Felix Friedrich,
Ryan Tsang,
Bertie Vidgen,
Alicia Parrish,
Chris Knotz,
Eleonora Presani,
Jonathan Bennion,
Marisa Ferrara Boston,
Mike Kuniavsky,
Wiebke Hutiri,
James Ezick,
Malek Ben Salem,
Rajat Sahay,
Sujata Goswami
, et al. (77 additional authors not shown)
Abstract:
The rapid advancement and deployment of AI systems have created an urgent need for standard safety-evaluation frameworks. This paper introduces AILuminate v1.0, the first comprehensive industry-standard benchmark for assessing AI-product risk and reliability. Its development employed an open process that included participants from multiple fields. The benchmark evaluates an AI system's resistance…
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The rapid advancement and deployment of AI systems have created an urgent need for standard safety-evaluation frameworks. This paper introduces AILuminate v1.0, the first comprehensive industry-standard benchmark for assessing AI-product risk and reliability. Its development employed an open process that included participants from multiple fields. The benchmark evaluates an AI system's resistance to prompts designed to elicit dangerous, illegal, or undesirable behavior in 12 hazard categories, including violent crimes, nonviolent crimes, sex-related crimes, child sexual exploitation, indiscriminate weapons, suicide and self-harm, intellectual property, privacy, defamation, hate, sexual content, and specialized advice (election, financial, health, legal). Our method incorporates a complete assessment standard, extensive prompt datasets, a novel evaluation framework, a grading and reporting system, and the technical as well as organizational infrastructure for long-term support and evolution. In particular, the benchmark employs an understandable five-tier grading scale (Poor to Excellent) and incorporates an innovative entropy-based system-response evaluation.
In addition to unveiling the benchmark, this report also identifies limitations of our method and of building safety benchmarks generally, including evaluator uncertainty and the constraints of single-turn interactions. This work represents a crucial step toward establishing global standards for AI risk and reliability evaluation while acknowledging the need for continued development in areas such as multiturn interactions, multimodal understanding, coverage of additional languages, and emerging hazard categories. Our findings provide valuable insights for model developers, system integrators, and policymakers working to promote safer AI deployment.
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Submitted 18 April, 2025; v1 submitted 19 February, 2025;
originally announced March 2025.
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Introducing v0.5 of the AI Safety Benchmark from MLCommons
Authors:
Bertie Vidgen,
Adarsh Agrawal,
Ahmed M. Ahmed,
Victor Akinwande,
Namir Al-Nuaimi,
Najla Alfaraj,
Elie Alhajjar,
Lora Aroyo,
Trupti Bavalatti,
Max Bartolo,
Borhane Blili-Hamelin,
Kurt Bollacker,
Rishi Bomassani,
Marisa Ferrara Boston,
Siméon Campos,
Kal Chakra,
Canyu Chen,
Cody Coleman,
Zacharie Delpierre Coudert,
Leon Derczynski,
Debojyoti Dutta,
Ian Eisenberg,
James Ezick,
Heather Frase,
Brian Fuller
, et al. (75 additional authors not shown)
Abstract:
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-pu…
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This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark.
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Submitted 13 May, 2024; v1 submitted 18 April, 2024;
originally announced April 2024.