Building agent applications and the systems behind them.
About · Focus · Open source · Projects · Contact
I build AI agent applications and contribute to the systems behind them: sandboxed execution, agent SDKs, tool use, and context management. My open-source work includes improvements to credential handling in OpenSandbox and proposed sandbox workspace protections in Google ADK. I am interested in the engineering details that make agents dependable in real workflows.
My longer-term interest is in agents that can build on previous experience. I am exploring agent memory and recursive self-improvement (RSI): how to preserve useful context, turn feedback into better behavior, and test whether an improvement holds up. Previously, I worked on video coding and visual computing.
- Agent applications · Current work
Building useful workflows around models, tools, and actions. - Sandbox & execution · Open-source work
Credential handling, workspace boundaries, and controlled agent execution. - Agent memory · Exploring
What should agents remember, retrieve, and carry forward across tasks? - Recursive self-improvement · Exploring
How can feedback improve an agent's harness, and how do we verify the gains?
Selected contributions to the tools and infrastructure behind AI agents.
| Project | Contribution | Stars |
|---|---|---|
| Strands Agents Harness SDK | Preserve structured output during history offloading. Merged · #5429 |
★ 8,998 |
| OpenSandbox | Harden C# and Kotlin SDK credential handling. Merged · #2183 · #2372 · #2375 |
★ 16,278 |
| MOMO CODE | Keep graph execution slots busy as tasks finish. Merged · #12 |
★ 1,107 |
| Google ADK | Contain relative paths within sandbox workspaces. Open PR · #7700 |
★ 22,047 |
| OpenHands SDK | Expose MCP resources and prompts in the SDK. Open PR · #6196 |
★ 1,322 |
| Qwen Code | Share compression cache for equivalent model selections. Open PR · #15318 |
★ 28,821 |
Repository stars and PR status checked on October 30, 2026.
An action execution layer for an agent harness: normalize and validate proposals, apply policy and required approval, execute permitted actions, and record redacted traces. Model calls and deployment isolation stay with the host application.
An auditable loop for improving a harness around a frozen model. Candidate changes are proposed, probed, contrasted, and composed from execution evidence; scoring happens only after freeze.
For conversations about agent applications, sandboxes, memory, or self-improvement:




