As the Department of Veterans Affairs (VA) advances its strategy to adopt high-impact, responsible artificial intelligence to improve services for Veterans, organizations across the federal health care ecosystem are aligning their own approaches to AI innovation.
At DSS, that alignment is taking shape through Athena, the company’s internal AI platform designed to streamline operations, break down information silos, and accelerate development workflows. By focusing on practical, secure, and high-value use cases, DSS is mirroring VA’s emphasis on using AI to drive measurable impact while maintaining strong governance and data protection.
Led by Josh Thomas, AI Architect at DSS, this transformation is enhancing how teams work across the organization, improving efficiency, and enabling faster delivery of mission-ready solutions.
In this Q&A, Josh shares how Athena is being used to enhance product development, unify access to information, and ultimately elevate DSS’ ability to support its customers with innovative, data-driven solutions.
Q: Tell us a bit about your role at DSS and your journey with Athena.
Josh: I’ve been with DSS for a while, and over the years, my role has evolved from senior development and integration coordination to creating tools that streamline processes. Athena, our internal AI solution, plays a huge part in this transformation.
Initially, I focused on making code creation easier, like generating unit tests automatically, but as AI tools evolved, I saw the potential for AI to break down information silos across the company using custom MCP tools to bring together information from systems that previously weren’t connected. This led to developing Athena into a system that supports everything from code generation to documentation and internal workflows, helping teams work more efficiently.
Q: What is Athena and how is it transforming internal operations at DSS?
Josh: Athena is an internal AI platform powered by Amazon Web Services (AWS) that we’ve built to automate and simplify internal processes, but it has evolved beyond a traditional assistant into a system of specialized AI agents.
One of our main goals is breaking down silos across tools like Jira, Confluence, Microsoft 365, and Azure DevOps. Athena brings that information together into a single interface where teams can quickly retrieve documentation, generate artifacts, and troubleshoot issues.
With recent updates, Athena can also integrate directly with systems like qTest and interact with live data, allowing teams not just to find information, but take action, such as querying or updating test artifacts without leaving the platform. We’ve also introduced user-managed custom AI agents that can be tailored to specific workflows and knowledge bases across DSS.
Ultimately, Athena reduces friction by automating repetitive tasks and enabling more connected, intelligent workflows, so teams can focus on higher-value work.
Q: It sounds like Athena is being used across many departments. Can you share some examples of how different teams are using it?
Josh: Absolutely. At this point, Athena is used across the entire organization.
One example is generating functional design documents after meetings, which can be a time-consuming process. Athena can take meeting notes, apply the right templates, and produce a near-final FDD, saving teams hours of manual work.
As Athena has evolved, teams are also using it to pull information from connected systems, generate documentation, and support day-to-day workflows across engineering, product, and business functions. With custom AI agents tailored to specific use cases, it’s becoming easier for different teams to streamline their work and get answers faster.
Q: How does Athena help DSS deliver more value to customers?
Josh: Athena gives us a significant edge in terms of speed and efficiency. We can develop products and respond to customer requests much faster than before.
It also allows us to quickly gather information and insights from across DSS and connected systems, whether that’s code repositories, product documentation, or operational data. By bringing that context together in one place, we’re able to make more informed decisions in less time.
As Athena continues to evolve, it’s not just helping us find information, but also acting on it within our workflows. The time saved on internal tasks translates directly into more focus on delivering value to our customers, which is key to staying competitive.
Q: What do you see as the future of Athena at DSS?
Josh: As the capabilities of models, agents and MCP tools keep expanding, Athena is moving from assisting with work to doing more of it. Today it helps teams retrieve information, generate artifacts, and act across connected systems. The next step is taking on more of the end-to-end work: turning a customer requirement into working, reviewable solutions with far less manual effort in between.
We're also continuing to connect Athena to more of the systems teams use every day, so more groups across DSS can bring their own data and workflows into the same place. The long-term goal is simple: help teams meet customer requirements and turn them into real, functional solutions faster, with the governance and oversight that work in health care demands.
Q: Finally, security is critical in health care. As Athena takes on more autonomous work, how does DSS keep it safe?
Josh: The more an AI system can do, the more you have to control where it runs and inspect what flows through it. We do both. All of our AI work runs in-house, inside our own Amazon Virtual Private Cloud, so sensitive data and DSS intellectual property never leave an environment we control.
On top of that, we're piloting Palo Alto Networks Prisma AIRS, an AI security layer that checks every prompt, model response, and tool call in real time for things like prompt injection, leaked health information, and unsafe tool inputs, stopping them before the model or tool ever runs. We're tuning it in staging now, before turning it on in production.
We back all of that with governance: an AI Oversight Committee that sets policy and decides what's ready to roll out, plus operational controls like per-user usage limits. As we give Athena more autonomy, the guardrails scale with it, so speed and safety move together.
We would like to thank Josh for sharing his insights with us! To learn more about how DSS is leveraging AI to drive innovation and deliver cutting-edge solutions, get in touch with us today. Subscribe to our blog page to keep abreast of the stories that matter to you.