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EvaluationReference
awesome-auditable-ai
by yzhao062
Curated resources for auditing and evaluating agent reliability and decision records
Python
Updated Sep 17, 2026
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Overview
Curates papers, tools, datasets, benchmarks, and standards for auditing AI agents. Organizes resources across reliability, monitoring, failure attribution, and decision-record practices to make audits actionable. Emphasizes auditing AI agents and reproducible evaluation artifacts and pointers to datasets and benchmarks for agent-to-agent and multi-agent settings reproducible evaluation artifacts.
The Value Proposition
As agents become more autonomous and interdependent, having a single map of auditing resources helps practitioners design trustworthy systems and evaluation pipelines. The challenge of fragmented literature and tooling is addressed by collecting evaluation patterns, benchmarks, and standards that surface agent failure modes and signal provenance for agent-to-agent evaluation. This list makes it easier to build A2A evaluation workflows and track agent track records over time.
Ideal For
Researchers and engineers designing evaluation strategies, benchmarks, or audit trails for multi-agent and agentic AI systems. This resource supports symmetry across governance and tooling for Multi-Agent Systems.
How It's Used
- Surveying benchmarks and datasets for multi-agent failure-mode analysis
- Finding tools and frameworks for decision records, provenance, and observability
- Designing A2A evaluation suites and reproducible audit pipelines
Topics
agent-evaluationagent-reliabilityagentic-aiai-agentsai-safetyauditableauditingawesomeawesome-listfailure-attribution+3 more
Keywords
agent-evaluationmulti-agent trustagent reliabilityauditable-ai