5 Qs Interview with Scott Dietzen, CEO of Augment – Center for Data Innovation
The Center for Data Innovation spoke with Scott Dietzen, CEO of Augment, a Palo Alto-based company that enhances software development with AI-driven automation. Dietzen discussed how Augment improves code quality and efficiency, how its platform helps developers write better software with fewer errors, and how Augment is shaping the future of AI-powered software engineering.
Hodan Omaar: What are some of the most exciting ways Augment’s platform leverages data to enhance software development?
Scott Dietzen: At Augment, we believe AI should do more than churn out lines of code. It should make software better: leaner, more maintainable, and aligned with how developers actually work. Instead of just predicting the next keystroke, AI should understand the bigger picture—how code fits together and how teams collaborate to improve software quality over time.
At Augment, we make this happen by using AI to delete code rather than just writing it. Most AI tools generate new code by default, often duplicating logic that already exists. The first time Augment recommended removing code rather than adding it, we knew this was a fundamentally different approach to AI-assisted development. Our AI technology analyzes the entire codebase and suggests deletions where code is redundant or unnecessary. Less code means fewer bugs, faster reviews, and more maintainable software.
We also enable the smarter reuse of code with context-aware AI. Traditional AI coding assistants function like new hires: they understand programming concepts but have no knowledge of your existing software. Augment, on the other hand, understands APIs, dependencies, coding patterns, and even undocumented best practices, allowing it to reuse and adapt existing code rather than generating redundant implementations. This reduces complexity and improves long-term software quality.
Another key feature is AI that spots issues before they become problems. Software failures cost more than a trillion dollars each year, according to Gartner. Augment analyzes vast amounts of development data to detect security vulnerabilities, inconsistencies, and performance bottlenecks before they impact production. It acts as an always-on safeguard, ensuring software is built to be secure, scalable, and resilient from the start.
Finally, we accelerate development without cutting corners. AI should help developers work faster while improving software quality, not just automate code completion. Augment provides real-time, contextually aware recommendations that help engineers make better decisions, avoid errors, and refactor intelligently. Instead of just speeding up development, it enables teams to write more maintainable, higher-quality code from the start.
Omaar: Can you share a standout example of how a client used Augment’s automation to overcome a complex software development challenge and improve their workflow?
Dietzen: A great example comes from developers at logistics company Lineage. They had already been using AI-powered coding assistants like GitHub Copilot, but they found those tools often suggested code that was syntactically correct but contextually wrong. This led to inconsistencies, duplication and extra review cycles. When they introduced Augment, the difference was immediately clear. Augment’s context-aware suggestions adapted to their internal coding patterns, enabling developers to move faster with less rework and fewer errors. The improvements in efficiency and accuracy were so significant that some developers we’ve spoken with said they’d personally pay for Augment’s license if their company didn’t provide it. That’s the kind of impact we aim for: AI that isn’t just another tool, but an indispensable part of how teams build and maintain great software.
Omaar: How does Augment adapt to different coding environments?
Dietzen: One of the biggest limitations of traditional AI coding tools is their one-size-fits-all approach. They rely on generic training data and struggle to adapt to the unique structure, patterns and best practices of a given software team. Augment is built differently. Instead of treating all code the same, Augment learns from the software itself: its APIs, dependencies, architecture, and undocumented conventions, without ever training on proprietary code. It dynamically retrieves relevant context, ensuring that suggestions match the team’s specific coding environment, whether it’s a complex microservices system, a large monolithic codebase, or an evolving hybrid model.
Augment also integrates seamlessly into the developer’s workflow, supporting multiple languages and frameworks. Whether a team is writing in Python, JavaScript, Go, or a mix of several languages, Augment adapts by recognizing how the code is structured and how developers interact with it. This flexibility is key to maintaining speed and accuracy across different environments, making Augment a true extension of the development team rather than a generic code generator.
Omaar: Automation in software development requires a balance between speed and accuracy. What role does data play in ensuring Augment’s AI-powered systems deliver fast yet precise results?
Dietzen: Speed in software development is meaningless if it introduces more bugs, inconsistencies, or technical debt. That’s why Augment’s AI doesn’t just generate code quickly, it ensures that code aligns with the project’s structure and minimizes errors. The key to achieving this balance is real-time data retrieval.
Augment also prioritizes incremental automation by breaking down tasks into manageable changes rather than making sweeping, high-risk modifications. Instead of overwhelming developers with large changes, it suggests small, targeted updates that keep the software stable while speeding up development. By leveraging contextual data in real-time, Augment ensures that automation enhances software quality rather than undermining it.
Omaar: Looking ahead, how is Augment positioning itself to lead in this space?
Dietzen: AI in software development is at an inflection point. The next wave of innovation won’t just be about writing code faster, it will be about building smarter, more resilient software that also reduces complexity. One of the biggest shifts will come from AI that understands software holistically, not just at the level of isolated code snippets. Future advancements will focus on full-system, intelligent AI that can reason across an entire codebase, recognize architectural patterns and proactively identify weak points before they become issues. Instead of reacting to developer input, AI will become more predictive, helping teams make better long-term decisions about refactoring, security and scalability.
Augment is positioning itself at the forefront of this shift. We see a future where AI becomes an active collaborator, helping developers manage the lifecycle of software from design to deployment to maintenance. In the coming years, automation will go beyond code generation and into automated testing, security vulnerability detection, and intelligent debugging. Augment is already laying the groundwork for this, ensuring that AI in software development isn’t just about speed, it’s about building better, more maintainable software from day one.
