paperarXivTrust 82 · PrimaryPublished 7d agoLive · 4d ago
Govern the Repository, Not the Agent: Measuring Ecosystem-Level Risk in AI-Native Software
Autonomous coding agents now open and merge pull requests in shared repositories at scale, and the field evaluates them the way it has always evaluated components, one agent at a time, on isolated benchmark tasks. Yet agents that each pass their own tests still leave repositories that accumulate problems no single contribution accounts for. We ask whether this problem belongs to the individual agent or to the repository where it accumulates. We study integration friction, the cost of integrating a contribution into a codebase that other contributors are concurrently changing. Across more than
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newsOpen-source agent framework crosses 50k starsnewsProduction-grade AI agents for financial compliance: Lessons from StripenewsInvesting in multi-agent AI safety researchnewsGoogle DeepMind is worried about what happens when millions of agents start to interactnewsSupporting Europe’s work in ensuring a trustworthy AI ecosystem
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repoAndrewDryga/coopnewsSupporting Europe’s work in ensuring a trustworthy AI ecosystemnewsOpen-source agent framework crosses 50k starsrepoyuxiaopeng/Github-Ranking-AInewsInvesting in multi-agent AI safety researchrepoNayjest/GitonewsGoogle DeepMind is worried about what happens when millions of agents start to interactnewsScarfBench: Benchmarking AI Agents for Enterprise Java Framework MigrationnewsProduction-grade AI agents for financial compliance: Lessons from Stripe
