AI DevSummit 2025 + DeveloperWeek Leadership 2025

Kevin Van Gundy, Hypermode, Founder & CEO

We've made AI more complicated than it needs to be.

With buzzwords and obtuse new concepts, we've made it too difficult for the average developer to reason about agentic systems. While we're writing new kinds of programs, they're still programs nonetheless, and the core concepts of a well-architected application still hold. Fundamentally, we're attempting to write better business process automation just at a scale we've never seen before.

In this talk, Kevin will give attendees a simple, non-technical, non-buzzword-filled framework for looking at existing problems within their business and reimagining them as multi-agent systems.

We'll start by breaking down existing workflows into jobs to be done and the data needed to do those jobs. Then, map those into agents and tools. We'll then go over strategies for incrementally implementing these systems that control for output quality.

Debo Dutta, Nutanix, Chief AI Officer

Generative AI is reshaping industries, from healthcare to finance and beyond, offering transformative opportunities for innovation. However, deploying it effectively in enterprise settings requires navigating complex challenges around scalability and security. Debo Dutta, VP of Engineering & AI at Nutanix and founding member of MLCommons—an AI consortium advancing technology for global impact– shares his insights on integrating GenAI seamlessly while ensuring robust infrastructure and safeguarding model integrity. Debo delves into the tech stack essentials, including foundational models, fine-tuning frameworks, deployment tools, and governance systems needed to tailor to diverse business needs across industries. 

Anil Inamdar, NetApp Instaclustr, Global Head of Data Services 

AIOps—having experienced the ups and downs of the hype cycle over the past few years—is now buoyed by rapid AI/ML advances and destined to reach its potential in 2025 and beyond. This means transformative change for how teams think about data and analytics, as maturing ML-powered (and open source) solutions take on and mitigate the complexities of database management. Teams doing their human-best to achieve performant queries through data traffic pattern analysis and keeping tabs on storage growth can now be more confidently helped by ML decision-making.

The AIOps dream is inevitable as ML training sets improve. Automated operations and predictive remediation, including optimized data indexes, reindexing and storage management based on predictive models, is arriving—and this AI DevSummit talk will discuss how to make it all a reality.

Abhai Pratap Singh, Amazon, Senior Product Manager-Technical

"From Technical Possibility to User Value: Product Management Strategies for Successful AI Implementation" explores the delicate balance between advancing AI capabilities and maintaining genuine user value. Drawing from experience leading Alexa's voice assistant development, this session reveals practical strategies for putting user needs at the center of AI product development. Learn how to avoid the trap of 'tech for tech's sake' while driving meaningful innovation. Through real-world examples from voice AI and multi-modal interactions, discover frameworks for evaluating AI features, measuring user impact, and making strategic decisions that enhance rather than complicate the user experience. Perfect for product leaders navigating the AI revolution.

Logan Ramalingam, Google Cloud, Head of AI Solutions

This session will talk about practical agent usecases for the enterprise.
 

Rob Whiteley, Coder, CEO

With great power comes great responsibility—and yes, coding agents can backfire if they’re not thoughtfully deployed. Instead of boosting productivity, they can frustrate developers, slow down progress, and even diminish your competitive edge.

Join Coder CEO, Rob Whiteley, as he unpacks three essential strategies organizations should embrace when scaling agentic coding agents enterprise-wide. Drawing from firsthand lessons and internal experiments—where agents effectively resolved nearly half the tasks we threw at them—we’ll share real-world scenarios showcasing engineering work AI agents excelled at. We’ll also highlight where they fell short, detailing what went wrong and what we’d do
differently next time.

You’ll walk away understanding:

* How realistic expectations ensure AI boosts rather than hinders productivity.
* Why effective human-agent collaboration is critical for developer satisfaction and innovation.
* How clear governance frameworks can prevent technical debt and keep productivity flowing.

Learn from our experiences and ensure your organization’s deployment of AI coding agents empowers your developers, ignites innovation, and solidifies your market leadership.

Shomron Jacob, Iterate.ai, Head of Applied Machine Learning & Platform

This AIDev Summit session will navigate an increasingly pivotal crossroads: the decision between investing in proprietary, custom-tailored Large Language Models (LLM) or capitalizing on the versatility and ease of public generative AI services.

The session will begin by demystifying the complexities of private LLMs. With domain-specific capabilities and enhanced data security, these models have faster customization and compliance with industry-specific regulations. Yet, they also pose challenges: a bigger investment, infrastructure requirements, and ongoing maintenance. These elements necessitate a thorough examination.

Next, the session will scrutinize public generative AI services, exploring the inherent benefits of these ready-to-use solutions. With their scalability, diverse applications, and lower upfront costs, they hold significant appeal. But they also come with their own set of considerations, such as data privacy, standardized performance, and reduced control over the model’s behavior.
With real-world examples, we will walk through how various organizations have approached this decision, the results they achieved, and the invaluable lessons learned.

The session will then go into a decision-making framework, with the purpose of enabling attendees to assess their options between private LLMs and public generative AI services more effectively.

Kevin Van Gundy, Hypermode, Founder & CEO

We've made AI more complicated than it needs to be.

With buzzwords and obtuse new concepts, we've made it too difficult for the average developer to reason about agentic systems. While we're writing new kinds of programs, they're still programs nonetheless, and the core concepts of a well-architected application still hold. Fundamentally, we're attempting to write better business process automation just at a scale we've never seen before.

In this talk, Kevin will give attendees a simple, non-technical, non-buzzword-filled framework for looking at existing problems within their business and reimagining them as multi-agent systems.

We'll start by breaking down existing workflows into jobs to be done and the data needed to do those jobs. Then, map those into agents and tools. We'll then go over strategies for incrementally implementing these systems that control for output quality.

Debo Dutta, Nutanix, Chief AI Officer

Generative AI is reshaping industries, from healthcare to finance and beyond, offering transformative opportunities for innovation. However, deploying it effectively in enterprise settings requires navigating complex challenges around scalability and security. Debo Dutta, VP of Engineering & AI at Nutanix and founding member of MLCommons—an AI consortium advancing technology for global impact– shares his insights on integrating GenAI seamlessly while ensuring robust infrastructure and safeguarding model integrity. Debo delves into the tech stack essentials, including foundational models, fine-tuning frameworks, deployment tools, and governance systems needed to tailor to diverse business needs across industries. 

Anil Inamdar, NetApp Instaclustr, Global Head of Data Services 


AIOps—having experienced the ups and downs of the hype cycle over the past few years—is now buoyed by rapid AI/ML advances and destined to reach its potential in 2025 and beyond. This means transformative change for how teams think about data and analytics, as maturing ML-powered (and open source) solutions take on and mitigate the complexities of database management. Teams doing their human-best to achieve performant queries through data traffic pattern analysis and keeping tabs on storage growth can now be more confidently helped by ML decision-making.

The AIOps dream is inevitable as ML training sets improve. Automated operations and predictive remediation, including optimized data indexes, reindexing and storage management based on predictive models, is arriving—and this AI DevSummit talk will discuss how to make it all a reality.

Abhai Pratap Singh, Amazon, Senior Product Manager-Technical

"From Technical Possibility to User Value: Product Management Strategies for Successful AI Implementation" explores the delicate balance between advancing AI capabilities and maintaining genuine user value. Drawing from experience leading Alexa's voice assistant development, this session reveals practical strategies for putting user needs at the center of AI product development. Learn how to avoid the trap of 'tech for tech's sake' while driving meaningful innovation. Through real-world examples from voice AI and multi-modal interactions, discover frameworks for evaluating AI features, measuring user impact, and making strategic decisions that enhance rather than complicate the user experience. Perfect for product leaders navigating the AI revolution.

Logan Ramalingam, Google Cloud, Head of AI Solutions

This session will talk about practical agent usecases for the enterprise.
 

Rob Whiteley, Coder, CEO

With great power comes great responsibility—and yes, coding agents can backfire if they’re not thoughtfully deployed. Instead of boosting productivity, they can frustrate developers, slow down progress, and even diminish your competitive edge.

Join Coder CEO, Rob Whiteley, as he unpacks three essential strategies organizations should embrace when scaling agentic coding agents enterprise-wide. Drawing from firsthand lessons and internal experiments—where agents effectively resolved nearly half the tasks we threw at them—we’ll share real-world scenarios showcasing engineering work AI agents excelled at. We’ll also highlight where they fell short, detailing what went wrong and what we’d do
differently next time.

You’ll walk away understanding:

* How realistic expectations ensure AI boosts rather than hinders productivity.
* Why effective human-agent collaboration is critical for developer satisfaction and innovation.
* How clear governance frameworks can prevent technical debt and keep productivity flowing.

Learn from our experiences and ensure your organization’s deployment of AI coding agents empowers your developers, ignites innovation, and solidifies your market leadership.

Shomron Jacob, Iterate.ai, Head of Applied Machine Learning & Platform

This AIDev Summit session will navigate an increasingly pivotal crossroads: the decision between investing in proprietary, custom-tailored Large Language Models (LLM) or capitalizing on the versatility and ease of public generative AI services.

The session will begin by demystifying the complexities of private LLMs. With domain-specific capabilities and enhanced data security, these models have faster customization and compliance with industry-specific regulations. Yet, they also pose challenges: a bigger investment, infrastructure requirements, and ongoing maintenance. These elements necessitate a thorough examination.

Next, the session will scrutinize public generative AI services, exploring the inherent benefits of these ready-to-use solutions. With their scalability, diverse applications, and lower upfront costs, they hold significant appeal. But they also come with their own set of considerations, such as data privacy, standardized performance, and reduced control over the model’s behavior.
With real-world examples, we will walk through how various organizations have approached this decision, the results they achieved, and the invaluable lessons learned.

The session will then go into a decision-making framework, with the purpose of enabling attendees to assess their options between private LLMs and public generative AI services more effectively.