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https://github.com/orico/flexeegile

Extending Agile For AI & Data Teams
https://github.com/orico/flexeegile

agile ai data data-science flexeegile methodology

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Extending Agile For AI & Data Teams

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README 

        
![Flexeegile - Extending Agile For AI & Data Teams

](flexeegile_banner.png)



[Flexeegile](https://www.flexeegile.com/) is my proposed extension to the well-established Agile framework, it aims to address the unique challenges posed by AI & Data projects in today’s complex computing environments.



Flexeegile, much like Agile, recognizes the value of the items on the right but considers the items on the left to be of higher value.



Flexeegile is not a replacement for Agile, but rather an observation and adaptation of Agile principles to suit the modern era of computing. 



As one of the founders of the Agile Manifesto, puts it:



“Flexeegile is not replacing, but observing that the nature of computing is richer than it was in the era of Agile when it was about desktop computers. Agile is still as relevant as ever.”



Flexeegile recognizes that today’s computing landscape extends far beyond desktop applications, encompassing cloud computing, big data, machine and deep learning, and more. It aims to be wide and interpretable, yet intentionally vague to remain future-proof as technology advances.



The name "Flexeegile" signifies the ability to interpret Agile methodologies in a flexible manner, especially in the context of AI.



Flexeegile is open to contributions on GitHub. Show your support by starring the repository!



## Core Values



The following is my interpretation of the values at this point in time, and your understanding may differ.



### 1. Uncertainty over Predictability 

"Embracing the inherent unpredictability in complex AI & Data systems." 



The concept of embracing uncertainty over predictability in AI and data systems reflects a paradigm shift from deterministic systems to probabilistic ones. While traditional engineering was focused on precise, repeatable outputs. AI tools such as Large Language Models (LLMs) embrace uncertainty to deliver transformative capabilities, in which the focus is on maximizing utility while effectively managing and mitigating risks associated with uncertainty. This approach acknowledges that achieving groundbreaking results often requires accepting a degree of unpredictability.



For example, in the evolving landscape of LLMs, where prompts play a central role, unit tests for prompts often produce variable outputs. Traditionally, unit tests have relied on deterministic results, which creates a need for new evaluation methods that account for variability. This shift requires accepting that outputs may only approximate a desired result and that not all tests will consistently produce the same outcome.



### 2. Data & Validation over Intuition & Belief

"Prioritizing evidence-based decision-making."



Intuition is invaluable at the start of any journey, guiding initial exploration and shaping potential paths. Belief helps set the course, providing direction and purpose. However, to make meaningful progress, we need insights derived from trusted AI systems. 



Testing and validating both the data and AI systems we use are core essentials for ensuring accuracy and reliability. Balancing quality and utility is crucial, and managing them enables us to unlock AI's full potential and make informed, impactful decisions that we can trust.



### 3. Simplicity & Clarity over Complexity & Noise

"Striving for clear, understandable solutions in a world of increasing complexity."



The principle of valuing simplicity and clarity over complexity and noise becomes increasingly critical as AI systems grow more sophisticated. There's a fundamental need to ensure that their design and functionality remain transparent, understandable, and accessible. 



This approach means creating solutions that can be readily comprehended by both technical experts and general users, avoiding the pitfalls of opaque "black box" systems that obscure their inner workings. By prioritizing clear, comprehensible architectures and decision-making processes, we can build AI technologies that are not just powerful, but also trustworthy and debuggable. 



When complexity inevitably arises, the goal is to maintain a core of simplicity that allows for quick root cause analysis, effective troubleshooting, and a genuine understanding of how and why an AI system produces its outputs, thereby preserving human agency and insight in an increasingly automated world.



For more information, visit [Flexeegile](https://www.flexeegile.com/).



Dr. Ori Cohen.