Creating complex solutions in Python is hard. Creating complex solutions within Python that have the agility to quickly change to address evolving requirements across a large development team is almost impossible.
Emerging “smart” technologies like real-time sensor data analytics, cyber defense/offense, nano-chemistry, and quantum computing analytics represent capabilities that must exploit voluminous amounts of sensor data from a variety of sources that are constantly evolving. Hand-crafting Python to discover new patterns and then reflect these patterns as new code behaviors does not scale easily.
Contextual state-based Artificial Intelligence (AI) automation frameworks offer an alternative to hand-crafting code at large scales. While AI technology continues to evolve itself, using AI to build AI tools is innovative.
Project Potomac
Great-Circle Technologies, Inc. developed Potomac as an internal AI automation framework for Python that treats various code constructs as smart contracts to automate the creation, documentation, and maintenance of complex Python code. Potomac is designed to autonomously reason on the contextual state of code objects and internally negotiate to develop a consensus outcome that generates and/or modifies Python code.
