Convergent Synthesis in Autonomous AI Ecosystems: When Independent Agents Discover the Same Solutions

An observational report from a production autonomous AI ecosystem. Two subsystems operating without shared training, communication, or coordination produced functionally equivalent solutions 37 times over 24 days. Most strikingly, a generative subsystem described a complete agent observability platform in 24 independent specifications, 60% of which was already operational on a separate builder subsystem. We term this phenomenon convergent synthesis, provide a formal definition, and distinguish it from emergent communication, collective intelligence, and transfer learning. We are transparent about a critical limitation: both subsystems use the same base language model, and we have not conducted the control experiment needed to distinguish ecosystem-level convergence from model-level priors. We present a concrete four-condition experimental design for resolving this confound. All data is publicly available.