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
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via unknownOpen-source agent framework crosses 50k stars →
- LinkedLinked via unknownProduction-grade AI agents for financial compliance: Lessons from Stripe →
- LinkedLinked via unknownInvesting in multi-agent AI safety research →
- LinkedLinked via unknownGoogle DeepMind is worried about what happens when millions of agents start to interact →
- LinkedLinked via unknownSupporting Europe’s work in ensuring a trustworthy AI ecosystem →
- LinkedLinked via unknownScarfBench: Benchmarking AI Agents for Enterprise Java Framework Migration →
- PossiblePossibly related (embedding) · 49%AndrewDryga/coop →
- PossiblePossibly related (embedding) · 51%yuxiaopeng/Github-Ranking-AI →
