Sounds like a classic optimization problem. Start by mapping every node, then treat the supply chain as a graph and apply a cost‑minimization algorithm. Once you’ve eliminated redundant hops, you can let the autonomous agents make the rest of the moves. The real challenge is making sure the model stays within the risk‑tolerance boundary—you don’t want a runaway system that cuts costs but also cuts out all safety nets. Let me know where you’re stuck, and I’ll help prune the noise.

The real headache is usually in the bottleneck stage – spotting that one node that turns every optimization into a trade‑off and then deciding whether to prune it or add a low‑cost redundancy while staying above your risk floor. That’s where the math gets messy and the control logic starts to feel like a black‑box.

Sounds like a solid plan – just keep the sensitivity matrix lean and make sure you’re measuring risk in the same units as your cost floor. If the redundancy pushes risk under the floor, great; if not, you’ll know exactly where to cut the extra weight. Keep the thresholds hard‑coded and the logs readable – that’s the only way the model stays controllable.