Agent Runtime
I built an agent runtime around the work that surrounds a model response: choosing a provider, preserving state, controlling tool execution, and handling failures. The goal is a system whose behavior can be inspected and managed—not just another chat interface.
My role
I defined the requirements and operating policies, implemented the runtime with AI coding tools, and worked through testing and failure cases.
Status
Local developer tool
Capabilities
Automation · Systems design · Technical execution
The problem
AI-assisted work involves more than getting an answer. A useful system needs to handle interruption, decide which capabilities a task requires, and distinguish a proposed action from permission to execute it.
What I worked on
Local-first infrastructure combining model routing, persistent state, and controlled tool execution. The case study focuses on how these pieces support practical development work and make failures easier to investigate.
Decisions and tradeoffs
Route for the task
Use task requirements to inform model selection rather than treating every request as the same workload.
Preserve enough state to continue
Important task context should survive a failed call or interrupted session instead of depending entirely on a fresh conversation.
Make execution boundaries explicit
Tool access and review requirements should be determined by the configured policy. A model proposing an action is not, by itself, a reason to execute it.
Evidence and scope
The evidence should be read as a demonstration of the particular workflow and configuration shown. It is not a guarantee that every possible action is safe or that every deployment uses the same settings.
What this shows
The ability to turn a recurring technical problem into infrastructure, reason through failure modes, and connect implementation choices to cost, reliability, and day-to-day usability.
See the work
Illustrative interface preview · synthetic data