AI-Native Software Ecosystems as Complex Adaptive Systems
A recent study published on arXiv (2604.19827) posits that software ecosystems designed for AI should be examined as complex adaptive systems (CAS). The paper emphasizes that emergent characteristics, such as architectural entropy, cascade failures, and comprehension debt, stem from the interactions among agents rather than from the components themselves. The authors align Holland's six CAS characteristics with observable dynamics in ecosystems, setting them apart from microservices and open-source networks. They introduce micro-level state variables, coarse-graining functions, and a manageable framework for measuring causal emergence. Furthermore, seven testable propositions connect CAS theory to software evolution, either challenging or expanding upon Lehman's laws, noting that multi-agent AI systems can fail in unexpected ways despite individual agents functioning correctly.
Key facts
- Paper published on arXiv with ID 2604.19827
- Argues AI-native software ecosystems are complex adaptive systems
- Emergent properties include architectural entropy, cascade failures, comprehension debt
- Maps Holland's six CAS properties onto ecosystem dynamics
- Distinguishes from microservices or open-source networks
- Defines micro-level state variables and coarse-graining functions
- Seven falsifiable propositions challenge Lehman's laws
- Multi-agent AI systems fail due to agent interactions, not individual errors
Entities
Institutions
- arXiv