DARWIN tracks fitness across generations, mutates strategies that work, prunes strategies that don't, and compounds intelligence over time. Think Karpathy's Autoresearcher — but production-ready, multi-agent, and wired into your entire operational stack.
Fitness scoring per run. Every agent execution produces a measurable outcome — P&L, engagement rate, conversion score, threat detection accuracy. DARWIN captures it all.
After N runs, assess generation performance. Statistical significance testing across the cohort. No premature optimization — DARWIN waits for real signal.
LLM-powered mutation engine. Mutate winning prompts, crossover successful strategies, refine system instructions, hard-reset failed lineages. Not random — intelligent evolution.
Best strategies survive, weak ones get pruned. Tournament selection across the population. The fittest agents reproduce. The rest are composted.
Every agent in the fleet runs its own DARWIN loop. Compound intelligence curves stack across generations. Cross-agent memory sharing means one agent's breakthrough accelerates the entire fleet.
Each prediction agent evolves its thesis generation, confidence calibration, and position sizing across market cycles. G47 agents outperform G1 by 340%.
Hook structures, narrative arcs, and CTA placement evolve based on engagement signals. Every post makes the next one better.
Scoring models evolve their feature weights and threshold logic based on actual conversion outcomes. No more stale heuristics.
Threat response playbooks mutate and improve based on incident outcomes. Detection patterns sharpen across generations of real attacks.
Every day your agents don't evolve is a day your competitors' agents get smarter. DARWIN turns your fleet into a compound intelligence machine.