By now, we're all very familiar with the limitations of LLMs. Some of these, such as context windows and hallucinations can be mitigated by the march of progress or external techniques like Retrieval Augmented Generation (RAG). But there are many that remain embedded deeply in the DNA of LLMs, such as their inscrutability, their linear reasoning due to the transformer architecture, and of course alignment. We believe and seek to demonstrate that multi-agent systems, while composed of LLMs and other black-box models, can be constructed in a way to address each of these limitations, and hold a great deal of promise as the focus of future pursuits aiming to address these limits more effectively.

Diverging from the traditional software meaning of the word, we present Axon, a polymorphic agentic system with the ability to alter itself in response to the users needs and goals. Imagine a tool so powerful that it adapts seamlessly to your unique needs, allowing you to harness the true potential of LLM-powered functions. Regardless of your specific models, data, or application, Axon simplifies the complexities, presenting you with a tailor-made solution that truly fits.