We are thrilled to announce the initial launch of the [GitHub Well-Architected Framework](https://wellarchitected.github.com).

It combines design thinking with prescriptive recommendations, empowering leaders and GitHub administrators to achieve their highest priority business goals.

The site's content is driven by GitHub's customer-facing engineers, our expansive Partner community, and customer contributors, and is organized around the five key pillars of the GitHub Well-Architected Framework:

![Productivity, Collaboration, Application Security, Governance, Architecture](//images.ctfassets.net/wfutmusr1t3h/yGOZCRZIdkRSp0HCRMyt8/2365633676dcdc5932896c3a9f705556/Screenshot_2024-09-18_at_7.30.36_PM.png)

### Why Well-Architected?

Our customers don't go looking to acquire sets of features, they are looking to solve problems. The GitHub Well-Architected Framework is designed with exactly that in mind - focusing on the business goals that a software development platform can and should help you achieve, and providing opinionated guidance on how GitHub can be used to achieve those goals. With any decision comes trade-offs, and the framework is also designed to help you understand those trade-offs and make the best decisions for your unique situation. It has an active contributor community and its content library will continue to expand.

### The Framework at a glance

The GitHub Well-Architected Framework, or GitHub "WAF" for short, is structured in layers:

-   __Pillars__: Foundational topics that the framework aims to address. Each pillar is defined by a specific business outcome that is achieved by following the prescriptions of the pillar.

-   __Design principles__: Each pillar is supported by design principles that provide the "how" to achieve the "what" of each pillar, guiding teams toward achieving their goals effectively. For example, if you want to achieve the outcome of "Productivity", you should keep topics like Automation, Continuous Improvement, and Feedback at the forefront of your decision-making process.

-   __Checklists__: These are the actionable items that can be used to evaluate your implementation of the design principles and the overall pillar. The checklists are a great starting point for a current state assessment.

-   __Scenarios and recommendations__: These are articles centered on scenario-based recommendations and tradeoffs. They are designed to help you understand, with technical examples, the implications of the decisions you make and how they impact your organization.

The site's content is only growing from here. We are committed to continuously improving the framework with content that is driven by our customers' common scenarios and feedback.

We invite you to explore [wellarchitected.github.com](https://wellarchitected.github.com) and take advantage of the resources available. Whether you're a seasoned architect or just starting out, we hope you'll find the site helpful and inspiring.

### What's next?

In line with our vision from [last year's GitHub Universe announcement](https://github.blog/news-insights/product-news/universe-2023-copilot-transforms-github-into-the-ai-powered-developer-platform/), GitHub has been re-founded on Copilot, and GitHub Well-Architected is no exception! We are striving to make it even easier to find the answers and content that are most relevant to you, and our work toward a native Copilot experience will help with exactly that.

We are also working on a GitHub Well-Architected assessment tool that will accelerate an evaluation of your GitHub environment against the framework and pinpoint the most impactful opportunities for improvement.

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Announcing the launch of GitHub Well-Architected 🎉

Authors: Jakub Oleksy | [@JakubOleksy](https://github.com/JakubOleksy) and Matt Nigh | [@mattnigh](https://github.com/mattnigh)

It's a question that haunts engineering leaders, spawning an industry-wide obsession with metrics like lines of code written, pull request velocity, and deployment frequency. We've built dashboards, created frameworks, and spent countless hours trying to quantify developer output.

__But what if we're measuring the wrong things entirely?__

At GitHub, we've learned through scaling our own engineering teams that the best indicator of productivity isn't found in commit counts or deployment metrics---it's in developer satisfaction. Happy developers don't just write better code; they solve harder problems, collaborate more effectively, and build better products.

In this post, we'll explore why developer satisfaction isn't just a feel-good metric---it's a powerful tool for measuring and improving engineering productivity.

### The productivity measurement problem

Despite the tech industry's obsession with metrics and optimization, there's a striking paradox at its heart: *most organizations have no systematic way to measure developer productivity.*

This measurement vacuum isn't an oversight---it reflects the complexity of quantifying productivity where individual contributors might spend days solving problems that no one was even aware of when the week began.

The industry's attempts to tackle this measurement challenge have largely centered around deployment metrics popularized by DORA and others: 

-   __Deployment frequency (DF):__ This tells you how often code is getting shipped to production. High frequency indicates your team is iterating fast, delivering value, and unblocking work instead of letting it pile up.

-   __Lead time for changes (LT):__ Measures how long it takes to get code from commit to production. If this is slow, your process has friction---slowing down innovation and killing morale. The faster your lead time, the quicker you get ideas into users' hands and keep devs happy by letting them see their work live.

-   __Time to recover (TTR):__ Aka MTTR, this is how fast you can get things back to normal after an incident. Incidents will happen---no team is perfect. But if you can recover quickly, you're building a resilient system and protecting your team's (and users') sanity.

-   __Change failure rate (CFR):__ This is the percentage of changes that cause problems in production. Lower is better; it means you're shipping quality and aren't stuck firefighting all the time. If every deployment breaks things, it's a sign your systems and testing need help. Aim to reduce failure without slowing down, so your team can deploy with confidence.

While these metrics provide valuable operational insights, they capture only the mechanical aspects of software delivery--the "how fast" and "how often" of code deployment. They fail to account for crucial elements like satisfaction, innovation, and toil.

A team could excel at rapid deployments while building the wrong features or accumulating crippling technical debt. It's akin to measuring a restaurant's success solely by how quickly it serves food, while ignoring the quality of the cuisine or the opinions of the chefs that make the food.

> The most promising path forward may be counterintuitive: instead of focusing on direct productivity metrics, organizations should prioritize developer satisfaction. 

Research from [Dr. Nicole Forsgren](https://nicolefv.com/) and others [suggests that high-performing engineering organizations consistently correlate with high developer satisfaction scores.](https://queue.acm.org/detail.cfm?id=3454124) This makes sense--developers who feel equipped, empowered, and engaged are more likely to solve complex problems effectively, collaborate productively, and make sound technical decisions.

Developer satisfaction encompasses crucial factors that traditional metrics miss. Things like the quality of tools and workflows, the effectiveness of team structures, the amount of toil in daily work, and the balance between feature delivery and technical investment. 

When developers report high satisfaction, it indicates they have the resources, autonomy, and support needed to deliver their best work.

### Why developer satisfaction matters

At GitHub, we've discovered that developer satisfaction is the foundation of engineering excellence. Developer satisfaction isn't just about having a pleasant workplace---it's about creating an environment where people can do their best work. Said another way, happy developers are engaged developers. 

> When engineers feel supported and valued, they don't just write code---they invest themselves in solving your organization's hardest problems. They bring their full creativity to work, question assumptions, and push boundaries that others might accept as fixed.

This engagement naturally flows into better problem solving. A satisfied developer isn't just going through the motions of completing features or fixes; they're thinking deeply about architectural decisions, considering edge cases, and anticipating future needs. They're more likely to speak up when they see potential issues and propose innovative solutions rather than band-aid fixes.

But perhaps the most compelling aspect of developer satisfaction is its impact on retention. When developers are satisfied, they stay longer, building deep institutional knowledge and expertise that compounds over time. Each year a talented developer remains with your team isn't just another year of code---it's another year of accumulated context, refined judgment, and strengthened team relationships.

The simple truth is this: satisfied developers do their best work. Everything else---from code quality to system reliability to innovation---flows from this fundamental principle.

### The virtuous cycle

When you invest in developer satisfaction, something remarkable happens: you create a self-reinforcing cycle of improvement. At GitHub, we've witnessed this firsthand in how we build and evolve our own platform.

It starts with making meaningful improvements to your developer experience. These don't have to be massive overhauls---they can be as focused as streamlining a deployment process or automating a repetitive task. What matters is that developers see their pain points being addressed.

> When developers see their feedback turning into real changes, they become more engaged in the improvement process. They're more likely to provide detailed, thoughtful feedback about what's working and what isn't.

This high-quality feedback becomes the fuel for your next round of improvements. Your team can focus on changes that will have the most impact because you're working from real, ground-level insights rather than assumptions. Each improvement builds on the last, creating momentum that transforms your engineering culture.

The end result is a team that doesn't just adapt to change---it drives change. Your developers become active participants in shaping their work environment, leading to better tools, processes, and ultimately, better products.

### Conclusion

The path to exceptional engineering output isn't found in counting lines of code or measuring pull request velocity---it's built on the foundation of developer satisfaction. At GitHub, our journey has consistently shown that when developers are happy and engaged, everything else falls into place: code quality improves, innovation accelerates, and teams deliver better products.

Start measuring developer satisfaction today. Not next quarter, not after your next planning cycle---today. Begin with a simple survey, act on the feedback you receive, and watch how this fundamental shift in focus transforms your engineering culture.

If you don't know to start, begin by asking your developers some basic questions:

-   What are the top Developer Experience (DevEx) areas you think should be improved?

-   How satisfied are you with the tooling and workflows for your work in general?

-   How satisfied or dissatisfied are you with the following tooling and workflows around testing?

-   With regard to tools and workflows, what is your biggest pain point?

-   With regards to shipping changes, what is your biggest pain point?

-   Imagine you have a magic wand to improve one thing (even if it seems impossible) about our DevEx. What would it be?

If your organization leverages GitHub Copilot, consider adding questions like the below, [or use our survey](https://downloads.ctfassets.net/wfutmusr1t3h/66acuCKYqXme0aukY8Rn3x/a8c682946b0176db5860544ad85fffe7/2024-04-23-GitHub-CCI-LP-Copilot-Impact-Survey-NT-V003.pdf):

-   How satisfied are you with GitHub Copilot?

-   Which workflows do you want GitHub Copilot to help with?

-   Why are you not using GitHub Copilot?

-   Do you have any feedback on GitHub Copilot?

Developer happiness isn't just a nice-to-have; it's a critical success factor and a leading indicator of long-term productivity. When you prioritize developer satisfaction, you're not just investing in your team---you're investing in better software, stronger innovation, and a more resilient engineering culture.

In the end, it's simple: happy developers build better software. Start today, and see the difference it makes.

"How do we know if our developers are productive?"

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Why developer satisfaction is your best productivity metric

## AI accelerates more than just developer productivity

Since the general availability of our [AI assistant in GitHub's support portal in February 2024](https://github.blog/news-insights/product-news/copilot-in-github-support-is-now-available/), 60% of the cases presented to it were resolved, with the majority being solved in under 7 minutes. This investment is part of GitHub's commitment to leveraging AI across the Software Development Lifecycle - including crucial moments when engineers need support! Let's take a look at how engineers at GitHub used AI within our support business to better serve our customers.

## How GitHub improved customer support with AI

__Primary goal__

-   Improve GitHub's customer support experience by leveraging automation powered by AI.

__Support Portal Reach__

-   GitHub serves a global community of over 100 million developers. GitHub's [support portal](https://support.github.com) assists an average of 178,000 customers each month with product and support-related questions.

__Key objectives__

-   Enhance responsiveness.

-   Increase customer satisfaction.

-   Allow the support team to focus on more complex and long standing issues.

__Specific focus__

Streamline the resolution of level 1 support queries by making known solutions easily discoverable, providing customers with a more direct and efficient route to resolution.

__What was done__

To achieve these objectives, we developed and implemented an AI-powered conversational assistant. This assistant is designed to autonomously address routine inquiries, providing immediate assistance and surfacing known solutions more effectively for our customers. The model is grounded to GitHub's extensive product documentation, helping ensure that the AI provides accurate and relevant guidance based on our existing knowledge bases. Our development and deployment process was underpinned by [Microsoft Responsible AI principles](https://www.microsoft.com/en-au/ai/responsible-ai?ef_id=_k_CjwKCAjw3P-2BhAEEiwA3yPhwB3LqV50_7LnWPLwGDTFbovX-xSKxgnIs_zXWcnpmt6HW0162H2wVRoCHbgQAvD_BwE_k_&OCID=AIDcmmgp5f6cxq_SEM__k_CjwKCAjw3P-2BhAEEiwA3yPhwB3LqV50_7LnWPLwGDTFbovX-xSKxgnIs_zXWcnpmt6HW0162H2wVRoCHbgQAvD_BwE_k_&gad_source=1&gclid=CjwKCAjw3P-2BhAEEiwA3yPhwB3LqV50_7LnWPLwGDTFbovX-xSKxgnIs_zXWcnpmt6HW0162H2wVRoCHbgQAvD_BwE)

By integrating AI into this process, we aimed to reduce the number of steps and overall process friction that customers often face, while simultaneously allowing our support team to apply their expertise to more complex cases that require human ingenuity and deeper investigation.

The AI-powered conversational assistant was seamlessly embedded into the support portal as a fully optional feature where customers could choose to interact with the assistant if they desired. Those who opted to engage with the AI-assistant received immediate assistance. Additionally, customers were encouraged to provide feedback on their experience, helping us continuously improve the tool and better serve their needs.

__Impact of the AI-assistant__

Since the [General Availability (GA) release in February 2024](https://github.blog/news-insights/product-news/copilot-in-github-support-is-now-available/), 60% of the cases presented to it were resolved, with the majority being solved in under 7 minutes. This is equivalent to over 20,000 customers having their problems solved through Generative AI.

By resolving these queries, the AI assistant has allowed our support team to focus on more complex customer questions, where their expertise can have the most impact. We have seen customer trust and satisfaction in the system grow through increased usage since general availability.

__Future implementations__

GitHub support's AI assistant has been deployed into Copilot chat on GitHub.com. [This capability](https://docs.github.com/en/enterprise-cloud@latest/copilot/using-github-copilot/asking-github-copilot-questions-in-githubcom#currently-available-skills) helps GitHub users access product & support related information whilst remaining within their workflow.

We are currently developing further AI capabilities tailored specifically for our internal support team, aiming to further enhance their ability to deliver exceptional customer service. This tool will provide intelligent suggestions, assist with routine task automation, and offer quick access to relevant information, enabling our support staff to manage their workflow more effectively and address complex inquiries with greater ease. We are also planning to expand the knowledge base for the AI-assistant and refine its response capabilities to better handle a wider range of inquiries.

## Insights Gained

__The power of tailored AI experiences__

Throughout the development and deployment of the AI assistant, one significant lesson learned was the importance of tailoring experiences. Our engineers discovered that by optimizing the AI assistant based on customer queries---through tailored responses and context-specific guidance---we could significantly enhance its relevance and effectiveness across a broad range of use cases. For instance, developers and admins often require different types of support, and we've built the system to adapt accordingly. By taking into account the size and complexity of the user, we enable the AI assistant to deliver personalized assistance that addresses the specific needs of each user.

__Expanding innovation beyond productivity__

Another key takeaway is the potential for AI to drive innovation beyond just enhancing productivity. We encourage engineers to experiment with AI, pushing the boundaries of what's possible. Consider new ways in which AI could be leveraged to achieve novel customer and business outcomes, such as creating more personalized customer experiences. By thinking creatively about the possibilities, engineers can support their organizations to unlock new value and opportunities that go beyond the traditional scope of support automation. This proactive approach to exploring the capabilities of AI not only enhances customer satisfaction but also positions your organization to lead in their respective fields, using AI as a powerful tool for innovation.

## How to enhance your organization with AI

Bring a customer-centric approach to engineering with AI:

1.  Identify a customer pain point that may benefit from the power of AI.

2.  Define the benefits you want to deliver to your customer and any disbenefits you want to avoid and evaluate different AI solutions to address the customer pain point. Ensure you plan for tracking customer feedback and whether the solution is achieving the desired benefits.

3.  __Customize and implement your AI solution.__ Based on your evaluation, consider an initial high-level architecture proposal. It can be useful to undertake a proof of concept (POC) to test your proposal for the likelihood of meeting the target benefits. If your POC is promising, move into building for production ensuring ongoing testing as you build and that you meet any of your internal development processes and requirements. Once you have finished development and completed any release activities required by the team/organization, deploy to a small number of customers for initial feedback.

4.  __Review and iterate.__ Regularly collect feedback from your customers and internal stakeholders and scale your deployment of the solution as you grow confidence. Use feedback to refine the solution continuously, enhancing its effectiveness and ensuring it continues to deliver value.

## Looking forward to the possibilities ahead for GitHub Support

As we continue to refine and expand the usage of AI in GitHub Support, we are excited about the broader possibilities it presents for enhancing various aspects of our services. The success of AI in handling level 1 support queries has demonstrated the potential of automation to not only improve responsiveness and customer satisfaction but also to empower our support team to tackle more complex challenges.

Looking ahead, we see significant opportunities to extend the capabilities of AI in the realm of customer success:

- __Product usage guidance.__ AI could be adapted to assist users in navigating and utilizing GitHub's features more effectively. By offering real-time guidance and tips based on user behavior and context, AI could enhance the overall user experience, helping customers unlock the full potential of our platform.
- __Support data analytics.__ We envision AI playing a crucial role in support data analytics, where it can help identify common issues, trends, and patterns. This data-driven approach can provide valuable insights to both our support and product development teams, leading to proactive improvements in our products and services that directly benefit our customers. We recently published a [blog post](https://github.blog/ai-and-ml/machine-learning/how-github-harnesses-ai-to-transform-customer-feedback-into-action/) on how we harness AI to transform customer feedback into action.
- __Training and onboarding.__ Beyond supporting the support team, AI can be instrumental in the training and onboarding of new team members across the organization. By providing instant access to a wealth of knowledge, best practices, and contextual guidance, AI can help new employees quickly become productive, accelerating their integration into the team.

In conclusion, AI gives engineers an unprecedented opportunity to enhance both customer and business outcomes in an organization. GitHub will continue to explore these possibilities and collaborate closely with developers, and customers to ensure that AI continues to evolve and meet the ever-changing needs. If you haven't yet, try GitHub's Support AI-assistant by visiting the GitHub Support Portal and entering your query in the search bar. We value your feedback and would love for you to be part of this exciting journey as we continue to improve how we serve our community.

Want to learn more about the strategic role of AI and other innovations at GitHub? Explore [Executive Insights](https://github.com/solutions/executive-insights) for more leadership perspectives and thought leadership on the future of technology and business.

Will you join the call to use AI to accelerate your organization's success? The journey ahead is full of potential, and we are excited to realize these opportunities together.

GitHub Copilot empowers engineers to help their organizations achieve better business outcomes for their customers. But AI doesn't simply help engineers do the same work more quickly; it can help them get to places they haven't been able to get to before! We're excited to share how our GitHub customer success team has been using AI to better serve our customers.


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GitHub case study: Enhancing customer support with AI

This session will delve into real-world applications and a future where AI fortifies defenses and helps preemptively identify and mitigate emerging threats. This session is designed for developers, security experts, and executives alike.

Key takeaways:

- __Key Takeaway 1__: Turn "I don't know what I don't know" into a firm grasp of current challenges in application security.
- __Key Takeaway 2__: Walk away with an understanding of how AI and GitHub can help solve current challenges in application security.
- __Key Takeaway 3__: Learn actionable next steps to apply AI to your application security using GitHub.

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AI is far from reaching its peak in application security. How GitHub applies AI to help streamline remediation and enhance alert interpretability.

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  <iframe src="https://www.youtube.com/embed/xvDzKXASPSI" width="640" height="375" frameborder="0" allowfullscreen></iframe>
</div>

This session will delve into real-world applications and a future where AI fortifies defenses and helps preemptively identify and mitigate emerging threats. This session is designed for developers, security experts, and executives alike.

Key takeaways:

- __Key Takeaway 1__: Turn "I don't know what I don't know" into a firm grasp of current challenges in application security.
- __Key Takeaway 2__: Walk away with an understanding of how AI and GitHub can help solve current challenges in application security.
- __Key Takeaway 3__: Learn actionable next steps to apply AI to your application security using GitHub.

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Mission Copilot Autofix: Securing the world's software

This will explore how GitHub Advanced Security (GHAS), powered by GitHub Copilot, revolutionizes vulnerability remediation and security debt management. The speakers will showcase current capabilities that can help developers rapidly address vulnerabilities and preview upcoming Copilot Autofix expansions.

Learn how these innovations can transform security practices, enabling efficient vulnerability management and proactive security debt reduction, encouraging developers to both build and run code, and help quickly and more easily secure it.

__Key takeaways:__

- __Key Takeaway 1:__ Discover how GitHub Copilot solutions can help reduce security debt at scale.
- __Key Takeaway 2:__ Learn how GitHub is redefining "shift left" by working to identify and address vulnerabilities before they reach production.
- __Key Takeaway 3:__ Gain insight into upcoming Copilot Autofix features that aim to expand security issue resolution.

Software vulnerabilities accumulate over time, creating security debt. While traditional AppSec tools identify issues, fixing them remains challenging due to limited expertise and time.

<div class="embed-responsive embed-responsive-16by9">
  <iframe src="https://www.youtube.com/embed/4hXe7WBFzwo" width="640" height="375" frameborder="0" allowfullscreen></iframe>
</div>

This will explore how GitHub Advanced Security (GHAS), powered by GitHub Copilot, revolutionizes vulnerability remediation and security debt management. The speakers will showcase current capabilities that can help developers rapidly address vulnerabilities and preview upcoming Copilot Autofix expansions.

Learn how these innovations can transform security practices, enabling efficient vulnerability management and proactive security debt reduction, encouraging developers to both build and run code, and help quickly and more easily secure it.

__Key takeaways:__

- __Key Takeaway 1:__ Discover how GitHub Copilot solutions can help reduce security debt at scale.
- __Key Takeaway 2:__ Learn how GitHub is redefining "shift left" by working to identify and address vulnerabilities before they reach production.
- __Key Takeaway 3:__ Gain insight into upcoming Copilot Autofix features that aim to expand security issue resolution.

Resource Page for /security/mission-copilot-autofix-securing-the-worlds-software

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Found means fixed: Addressing security debt at scale

That's why the introduction of Copilot Autofix is a game changer. This feature provides suggested fixes for security issues and reduces the time and effort spent remediating alerts in your codebase. Learn how we're helping customers "get clean," quickly, all while streamlining collaboration between security and engineering teams.

__Key takeaways:__

- __Key Takeaway 1:__ See how Copilot Autofix cleans up historical alerts.
- __Key Takeaway 2:__ Learn about ways to remediate security debt at scale.
- __Key Takeaway 3:__ Witness how Copilot Autofix combines with our new tools to help eradicate vulnerabilities.

GitHub customers often deal with large backlogs of security vulnerabilities. These are time consuming to address and take focus away from new development.

<div class="embed-responsive embed-responsive-16by9">
  <iframe src="https://www.youtube.com/embed/th2wRmI3A8I" width="640" height="375" frameborder="0" allowfullscreen></iframe>
</div>

That's why the introduction of Copilot Autofix is a game changer. This feature provides suggested fixes for security issues and reduces the time and effort spent remediating alerts in your codebase. Learn how we're helping customers "get clean," quickly, all while streamlining collaboration between security and engineering teams.

__Key takeaways:__

__Key Takeaway 1:__ See how Copilot Autofix cleans up historical alerts.
__Key Takeaway 2:__ Learn about ways to remediate security debt at scale.
__Key Takeaway 3:__ Witness how Copilot Autofix combines with our new tools to help eradicate vulnerabilities.

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Pay down security debt with Copilot Autofix

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