Software Architecture Insights

GitHub published five incidents for August 2026. Three of them are the same failure.
On the 17th, seven and a half hours of degraded service: 29,000 organizations, 4.8 million requests failed or slow, 56% of front-door requests failing at the peak. Three weeks, three incidents, one blind spot — capacity ran out in places nobody was graphing.
Your autoscaler scales the thing you are observing. Saturation happens in the thing you don't.
In this episode:
The August 17th outage, and the two beside it on the 6th and the 26th
Sidecar concurrency, network flow tables, database primary headroom: limits with no dashboard
Why these limits are dangerous: someone else's layer, hard rather than soft, invisible
Retry amplification, and how one data center's capacity problem became an authentication outage everywhere
Protective throttles set so high they engaged after the damage was done
Two lists to build this week: your real hard limits, and your retry code

Links:
GitHub Availability Report: August 2026
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Atchison Academy courses

It's 3am and a payment service is failing. The engineer who answers has never touched it, and the next hour goes to finding someone who can fix it.
In this episode, Lee goes further than his article "When Everything Is Critical, Nothing Is." The article named the two missing decisions. This episode is about how to make them: which services matter most, and which one team owns each of them.
In this episode:
The four service tiers, in plain terms
Three questions that keep everything from landing in Tier 1
Why every tier is a budget decision, and why you should cap Tier 1
The dependency trap: a Tier 1 service is only as reliable as the lower-tier service it can't live without
What "owner" means: paged, planned, permitted
Inherited services, and an exercise to run this week

Links:
Read the original article
The ownership-gap diagnostic
STOSA
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Atchison Academy courses

Today, we discuss the significant impact of AI on software development, particularly the use of AI assistants like GitHub Copilot. Our guest, Burke Holland, a principal developer advocate at Microsoft, shares insights on how developers are increasingly integrating AI into their workflows. We explore the current spectrum of AI usage, from code completion to AI agents that can write code autonomously. Burke emphasizes the need for developers to adapt to these changes while maintaining a critical perspective on AI-generated code. This conversation sheds light on the evolving role of software developers in an AI-enhanced landscape.
In this insightful episode, Burke Holland, a principal developer advocate at Microsoft for the Visual Studio Code team, discusses the transformative role of artificial intelligence (AI) in software development. The conversation starts with the observation that developers have shifted from skepticism about AI's capabilities in coding to a more supportive stance, with many now using AI tools like GitHub Copilot to assist in their work. Burke explains how these AI tools can provide significant assistance through 'completions'—intelligent suggestions that understand the context of what the developer is working on, effectively acting as a cognitive partner in the coding process.
The discussion further explores the concept of AI 'agents,' which represent a cutting-edge approach to automating aspects of coding. Burke acknowledges the potential of these agents to undertake more complex tasks but also expresses concern about the implications for developers' roles. He emphasizes the importance of human oversight, suggesting that while AI can enhance efficiency, developers must remain engaged with the code they produce to ensure quality and comprehensibility. The episode highlights the balance between leveraging AI's strengths and maintaining the critical thinking skills that are essential in programming.
As the episode wraps up, Burke encourages developers to adopt an exploratory mindset toward AI tools. He notes that the landscape of software development is rapidly evolving, and by integrating AI thoughtfully, developers can enhance their productivity while still preserving their essential skills and understanding of coding. The conversation underscores a future where AI and human collaboration can lead to better software outcomes, emphasizing the need for a nuanced approach to AI integration.
Takeaways:
In the podcast, we discussed how AI is changing the software development landscape significantly.
Burke Holland highlighted that the use of AI in coding has seen a dramatic increase recently.
We talked about the importance of understanding the code produced by AI to maintain quality.
Completions in AI tools like GitHub Copilot can enhance a developer's workflow remarkably.
Burke shared insights on the difference between completions and agents in AI coding practices.
We concluded that while AI can write code, developers still play a crucial role in ensuring its accuracy.

Links referenced in this episode:
softwarearchitectureinsights.com

Companies mentioned in this episode:
Microsoft
VS Code
GitHub Copilot
JetBrains
Speckit

Today's discussion centers on the vulnerabilities associated with AI systems and the increasing threats they face. Our guest, Preston Wood, the Chief Security and Strategy Officer at Databox, highlights the lack of transparency in AI technologies as a significant factor that makes them more susceptible to attacks. We explore how this obfuscation creates challenges in understanding and defending against potential threats. As AI continues to advance, we also consider the evolving nature of phishing attacks and the importance of robust data management strategies to mitigate risks. This episode aims to provide insights for software architects and leaders on navigating the complexities of AI integration while ensuring security and reliability.
The podcast episode features an insightful discussion about the growing vulnerabilities associated with AI systems. The guest, Preston Wood, the Chief Security and Strategy Officer at Databox, addresses the surge in AI-related attacks, emphasizing the need for greater transparency and understanding of AI operations. He explains that the ambiguous nature of AI systems makes them appealing targets for attackers, who can exploit the lack of visibility into how these systems function. Throughout the conversation, Preston highlights the importance of ensuring that AI-generated data is clean and comprehensible to mitigate risks. He compares today's AI landscape to early phishing attacks, which have evolved into sophisticated threats due to advancements in AI technology. This episode serves as a crucial resource for software architects and technology leaders, offering them guidance on how to navigate the complexities of securing AI systems and understanding the implications of AI on data management and security practices.
Takeaways:
The podcast discusses the growing vulnerabilities associated with AI-based systems due to their lack of transparency.
Preston Wood emphasizes the importance of clean and understandable data for AI performance and security.
Organizations are advised to improve their data architecture to ensure AI projects are successful and not hindered by poor data quality.
The conversation highlights the evolving nature of phishing attacks, which are now more sophisticated due to AI advancements.
Effective security requires a layered approach that combines model training and guardrails for AI systems.
Listeners are encouraged to consider how well their organizations are integrating AI into their existing technology frameworks.

AI is no longer a novelty; it is now a significant part of our lives. In this podcast, we discuss the importance of using AI thoughtfully in software architecture. As architects, our role is not to chase the latest AI trends but to determine how AI can genuinely enhance our applications. We emphasize that AI should be viewed as a tool, not just a product strategy. We also provide guidance on evaluating AI requests, ensuring that the focus remains on achieving meaningful business outcomes while managing the complexities that come with AI implementation.
Software Architecture Insights provides a comprehensive overview of the evolving role of AI in software architecture as we enter 2025. The discussion begins with the acknowledgement that AI is no longer a novel concept, but a standard aspect of modern technology. Generative AI models and multimodal systems are now common, seamlessly integrating text, images, and speech. Despite the substantial growth in AI capabilities, the hype surrounding AI continues to escalate, with companies feeling pressure to incorporate AI into their products. This episode emphasizes that software architects must not succumb to this hype. Instead, their focus should be on ensuring that AI is used appropriately and effectively within their applications. The speaker stresses that AI should be viewed as a tool to enhance products rather than a mere trend to follow. This means that architects need to translate management’s desire for AI into actionable, realistic strategies that align with business goals and product visions.
The conversation delves into the responsibilities of a software architect, who must evaluate whether AI solutions genuinely address identified problems. This includes assessing the available data, understanding the types of AI needed, and determining the long-term operational costs associated with implementing AI features. The speaker underscores that not every problem requires an AI solution; sometimes, a simpler approach may suffice. For instance, traditional algorithms might be more efficient and effective than complex AI systems for certain tasks. As architects navigate these challenges, they are advised to engage in thoughtful discussions with leadership to align expectations and ensure that AI implementations are sustainable.
In conclusion, the episode provides practical guidelines for evaluating AI requests in a software architecture context. Architects are encouraged to clarify business goals, assess data readiness, explore simpler alternatives, and plan for compliance and sustainability. By doing so, they can ensure that AI is integrated into products in a meaningful way, ultimately bridging the gap between innovation and practicality. The key takeaway is that AI should enhance the product and align with business objectives rather than merely serving as a checkbox to satisfy market demands.
Takeaways:
In 2025, AI is a significant part of everyday software development and design.
Software architects must not only embrace AI but also understand its real implications and costs.
AI should enhance products and services, not be an unthoughtful addition driven by hype.
The role of the architect includes translating management's AI requests into meaningful technical strategies.

Links referenced in this episode:
softwarearchitectureinsights.com

Companies mentioned in this episode:
OpenAI