What Inspired Us: The inspiration for Marga was incredibly straightforward: there was a major problem in how supply chain disruptions are handled, and we found a solution. The manual scramble that logistics managers face when a disruption hits was costing businesses time and money, and we realized an autonomous agent was the exact answer to fix it.
How We Built Our Plan: The planning phase was heavily collaborative. Mapping out the architecture required extensive whiteboarding to lock down the exact data flow of our "Perceive, Reason, Act" loop. We deliberately chose to outline the backend logic and draft the UI/UX mockups for the dashboard and Slack integration early on to ensure rapid iteration and robust API handling.
A significant portion of our planning was dedicated to modeling the agent's core reasoning logic. We had to define exactly how Marga would weigh alternative routes against potential delays. We formalized this decision threshold in plain logic during our design phase to ensure consistency: the agent will recommend a reroute if the premium cost of rerouting is less than the probability of a stockout multiplied by the total cost of that stockout.
