Three Projects I’m Proud of in 2025

As the year winds down, I wanted to share three projects that I’m particularly proud of working on in 2025, each focusing on how AI is changing the way we build and deliver government digital services.

What’s kind of surprising to me is that all of this work happened in the second half of the year. That’s less a testament to my productivity and more a reflection of how quickly this space is changing. The tools and capabilities that made these projects feasible for me to do simply weren’t there six months earlier.

Designing for Delegation

This year I spent a lot of time thinking about what happens when AI agents can act on people’s behalf. Instead of citizens navigating complex government forms and processes themselves, what if they could delegate those tasks to an AI agent that handles the complexity for them?

I wrote about this idea in the post Designing for Delegation and built delegation.design as a resource for the design patterns government teams will need as this shift happens. The key takeaway for me is that we need to stop thinking only about designing traditional interfaces and start designing delegation relationships. We need to start considering how people establish boundaries, how they stay informed about what’s happening, and how they maintain control.

This is sort of related to another post I wrote with my colleague Dan Munz on the end of the Civic Tech’s interface era. People’s expectations for how they can interact with their government are changing. So we must change the way we design and build government digital services.

ATLAS ATO Accelerator

Getting software through the federal Authority to Operate process is painful. I started exploring how well-structured Terraform code could serve as compliance documentation. Focusing not just on infrastructure-as-code, but on compliance-as-code.

The ATLAS ATO Accelerator project takes this further by creating AI-readable instructions for FedRAMP compliance. The idea is that AI coding agents can use these instructions to generate infrastructure that’s compliant by default, dramatically reducing the time and effort agencies spend on security documentation.

SpecOps

Legacy system modernization is hard, and most AI-assisted approaches focus narrowly on translating old code into new code. But when AI translates code it doesn’t understand, there’s a high probability that errors will get baked into the new system.

The SpecOps approach flips this traditional understanding of legacy system modernization around: the software specification – not the software code – is the source of truth. Legacy modernizations should begin by extracting knowledge and documenting how a system is supposed to work. Then, that specification can be used to guide modernization via spec-driven development. The spec generated through SpecOps outlasts any particular technology stack, so when today’s modern code becomes tomorrow’s legacy system, agencies still have the authoritative system documentation.

All three projects share a common thread: using AI to make government technology work better, whether that’s improving citizen experiences, accelerating compliance, or modernizing legacy systems.

I’m excited to keep building on these ideas in 2026.

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