Prax / AgentOS and ApodexHarness
I joined Apodex in late March 2026 as one of three founding engineers of Prax / AgentOS and became the technical owner of ApodexHarness infrastructure. The project develops an execution system for complex, long-running tasks.
I built heavy_mode, which combines parallel sub-agents, cross-branch synthesis, global verification, and continuation after interruption. I proposed planner-generated, schema-constrained workflow DAGs and replaced a LangGraph dependency with a compact execution engine supporting branches and verification-retry loops.
I also developed token-budgeted context compression, file-based research memory, tool-specific result budgets, and evidence checks for unsupported numbers and citations. Reliability work included durable agent messages, sandbox isolation, tool permissions, and cancellation-safe event streaming.
The harness connects workflows to a unified evaluation interface. I supported the Apodex 1.1 and 1.2 model releases and ran 240 APEX Agents 1.1 tasks end to end to validate the evaluation path.
About Me
I am an AI Research Scientist on the post-training team at Apodex and one of the founding engineers of Apodex Harness and FrontierAgent. I work on reliable agent systems for long-running tasks: multi-agent orchestration, memory and context management, tool use, and verifiable evaluation. Previously, I led AI development at Watt, a Shanda-incubated startup. I also teach postgraduate deep learning at Nanyang Technological University. My earlier research focused on robot perception and visual SLAM.