Suggested Prompt

“We are uncovering better ways of developing software by doing it with the assistance of generative AI tools. Through this work we have come to value:

Individuals and interactions over processes and tools
Individuals and AI interactions over rigid roles and ceremonies
Working software over comprehensive documentation
Explainable, working software over comprehensive documentation
Customer collaboration over contract negotiation
Valuable solutions over contract negotiation
Responding to change over following a plan
Responding at pace over perpetuating legacy patterns

That is, while there is value in the items on the right, we value the items on the left more.”

We carefully evaluated each principle to determine if, why and how it should be changed to reflect collaboration with AI agents, tools and platforms in the software development lifecycle (SDLC).

1. Individuals and interactions over processes and tools
Individuals and AI interactions over rigid roles and ceremonies

The original manifesto’s ethos of people over process does not change in the AI-assisted manifesto, but we believe that AI interactions are just as essential as human interactions to improve these aspects of software development:

• Flexible roles and adaptation
  o   AI companion tools and training allow for more fluid roles and more skills per individual

• Cross-functional collaboration
  o   AI-driven efficiencies allow more time for creativity and innovation between functions

• Enhanced decision-making
  o   AI can make unbiased decisions, using insights to provide maximum value

• Adapting ceremonies and processes
  o   AI can run ceremonies

• Reducing admin
  o  AI can drive communications and reporting

2. Working software over comprehensive documentation
Explainable, working software over comprehensive documentation

If AI makes code explainable and auditable, the need for comprehensive documentation is greatly reduced. In the future, most code will be AI-generated rather than handwritten, necessitating an explanation for how it works. This shift moves us from traditional documentation, which shows how software functions, to explainability, which reveals why the software behaves in a certain way.

AI achieves this through several mechanisms:

• AI-written and governed code is inherently auditable and accurate, ensuring that every line of code is transparent and traceable
• Segregating code helps keep code organized to be regenerated, while AI-assisted prototypes allow for quick iterations and improvements
• AI-driven demos and training sessions provide practical insights into how the code operates

Additionally, AI can generate real-time explanations of live code, giving developers an immediate understanding of how systems are functioning. By collecting real data, AI offers rapid feedback, allowing for adjustments that enhance performance and reliability.

This approach bridges the gap between human and AI-generated code, making the software development process more transparent and efficient. AI assistance leads to quicker development cycles, enabling the early release of valuable features and reducing the time to market.

3. Customer collaboration over contract negotiation
Valuable solutions over contract negotiation

The original manifesto’s ethos of collaboration remains the same, but AI lets us bring value directly to the discussion. In the past, we’ve focused on fulfilling customer requests through collaboration. However, AI allows us to efficiently validate and ensure that these requests genuinely add value to customers. As the saying goes, if you ask customers what they want, they might just ask for a faster horse and cart—they don’t always know what’s possible with AI-powered technologies.

By implementing A/B testing and generating insights, AI can help prioritize the backlog based on data-driven decisions rather than customer opinions. AI excels at uncovering valuable insights that humans may overlook due to biases and preconceived notions, enabling a more effective and objective approach to prioritization. We believe AI will help clients come to value not just our teams but our tools as well.

4. Responding to change over following a plan
Responding at pace over perpetuating legacy patterns

The core facet of this value is to predict change, at pace, over solely reacting to the present.

Merely responding is no longer enough—responding at pace is the new standard of excellence. Consider this example: When a customer tweeted about an issue, the company's leadership responded within six hours, addressed the problem within six days and implemented a solution within six weeks.

It’s not just about fixing bugs; it’s about how quickly they are fixed.

To keep pace, it’s essential to build systems that enable faster responses, such as automating processes to accelerate workflows. Traditionally, a new feature would be added to a story, picked up by another team, developed and then deployed. However, using generative AI to automate steps—like auto-updating stories, regenerating, deploying changes and even ways of working and ceremonies—can drastically reduce human intervention and save time.