Salesforce evolves AI agent performance to 93% with DarwinX framework

Salesforce AI Research just published a framework that treats AI agent optimization like natural selection. Called DarwinX, it evolves the scaffolding around a language model, the prompts, tools, skills, and control flow, while leaving the model’s own weights completely untouched. The result: a real-task pass rate that jumped from 43.5% to 93.0% on a 1,260-task benchmark. That benchmark, WebArena-Infinity, measures how well an AI agent can complete actual web-based tasks. How DarwinX actually works DarwinX maintains a population of “harness variants,” each representing a different configuration of how the agent interacts with the world. It applies population-based natural selection to these variants, keeping what works and combining successful features from different lineages. The framework operates under what the 12-author research team calls a “preserve-and-extend” contract. Only changes that demonstrably improve performance survive to the next generation. An archive of previous lineages provides genetic material for recombination, preventing the system from losing hard-won capabilities while exploring new ones. Fitness isn’t judged by vibes. Each variant gets evaluated through s...