We built a recursively self-improving infrastructure for solving complex supply chain optimizations that monolithic LLM's overlook due to the multi-objective layer with conflicting constraints. Problems are decomposed into modular execution graphs that define communication, delegation, and operator assignment. As the system runs, it continuously restructures this topology, selectively breeding successful graphs together. A frontier meta-operator creates initial division of labor and a measurement program, then benchmarks these teams, keeping only the most successful one but storing the worse architectures in a database so not to repeat itself later. Then, using the most successful of architectures, the meta-operator tries to make it better by further mutating it with known best practices learned through continuous iteration. This process goes on until a certain benchmarking threshold is met for a team. Essentially, the system is better at a given problem with time because we encode the problem's solution strategy into the topology of operators itself. Our UI also makes the full workflow accessible to non-technical users, allowing them to monitor exactly how the custom infrastructure is evolving. Through our agent, “Bron,” users can describe their problem in plain language. Bron then guides them through defining the problem statement, asks the necessary follow-up questions, and helps manage custom files when needed. Bron effectively serves as the entry point to the system: it gathers the right context and turns it into a detailed, structured prompt that a typical user may not know how to write on their own. This approach lets us iteratively improve both the network and the solution for a given supply chain problem. In our F1 CO2 emissions reduction flight plan example, our novel method reduced carbon emissions compared with the baseline case. Because the F1 calendar spans many countries and tight race-week deadlines, the logistics network is complex: freight, staff, cars, parts, hospitality equipment, and other resources must arrive at each circuit on time. The baseline case represents a standard flight plan or routing strategy, while our method searches for improved routing, scheduling, and allocation decisions that reduce unnecessary travel, lower emissions, and still satisfy operational constraints.
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