The Seattle AI Landscape and Where My Work Fits In
Seattle has a particular kind of AI energy. It’s less about splashy demos and more about infrastructure: cloud platforms, large-scale data systems, and the glue that keeps everything running. From Magnolia, looking out over the city and Discovery Park, it’s hard not to see the skyline as a stack of systems.
In this post, I want to talk about how I see the Seattle AI landscape and where my own work in semantic memory, multi-agent orchestration, and protocol-first design fits into that ecosystem.
Seattle as an Infrastructure City
Seattle is home to some of the largest cloud and software organizations in the world. That shows up in the way people think about AI here:
- Scale and reliability matter just as much as raw capability.
- Systems thinking is the default, not an afterthought.
- There’s a long history of building tools that other builders rely on.
My own work feels aligned with that DNA: I care less about a single impressive model call and more about the invisible layers that make complex AI systems possible.
My Niche in the Seattle AI Ecosystem
My niche is AI systems architecture — specifically:
- How agents coordinate through a controller.
- How semantic memory is structured and reused.
- How we design protocols so behavior is explicit.
On philhills.com and in my GitHub repositories, that shows up as:
- Cube Protocol and other semantic memory experiments.
- Agent orchestration patterns and multi-agent controllers.
- Packet and protocol formats for AI-to-AI communication.
Magnolia, Discovery Park, and Thinking in Systems
Living in Magnolia, across from Discovery Park, has a strange but real influence on how I think. The park is a giant, structured space — trails, fields, coastline — that still feels organic and alive. Good systems architecture tries to do the same thing: create structure without killing the ability to adapt.
Coaching U12 girls soccer and training regularly at TruFusion Ballard give me similar lenses: teams, routines, and tiny adjustments over time. Those experiences shape how I approach AI systems — it’s all practice, patterns, and evolving structure.
Where My Work Can Help in Seattle
I see my work fitting best into organizations and projects that:
- Have complex workflows where AI is one component, not the whole product.
- Need durable memory across sessions, tools, and models.
- Care about being able to inspect, replay, and debug AI behavior.
- Operate on large, multi-domain datasets where structure matters.
In other words: places in the Seattle ecosystem that want AI to be part of a real system, not a fragile bolt-on.
How to Explore My Work from Here
If you’re here because you searched for “Phil Hills Seattle”, these are the best next places to look:
- About – who I am and what I focus on.
- Projects – system- and protocol-level work.
- Code – how this all shows up in open-source form.
- GitHub – the full repository list.
Seattle is full of people quietly building the foundations of the next wave of infrastructure. My work is one small piece of that puzzle: making sure AI systems have the memory, orchestration, and protocols they need to behave like real systems, not just clever tricks.