AgentBoundary AI: Secure AI Agent Execution

My three AMD AI League entries are all built on the mandated ROCm PyTorch base image and run Qwen3-VL-8B-Instruct in bf16 on the AMD GPU. A resident server loads the model once and listens on a unix socket, so each harness call to /app/app.py is a thin client that finishes inside the 30-second budget. The weights ship inside the images, and nothing is downloaded at evaluation time. Match 4, Wayfinder (web information retrieval). It reads a natural-language request, fetches the start page and ranks same-site links by how well they match the question. Each page is cleaned to text, with tables kept as rows. The model then either gives the value with the passage it came from, or picks the next link to open, for up to three hops. An answer only counts if it appears on a cited page, and if nothing is found the output is an empty answer. It also works on a saved copy of a site (--corpus). Match 2, Lumen OCR, reads licence plates and road signs. On Chinese plates it keeps the province character; on US plates it drops the state banner and slogan. Multi-line signs are joined in reading order, and when the first pass is unsure about a hard image (glare, blur, noise) it reads an enhanced copy again. Match 3, Provenance RAG, indexes PDFs, Word files, spreadsheets, CSVs, logs, source code and images (read by the vision model) once. Empty folders, unreadable files, encrypted PDFs and unknown types are skipped. Each answer cites only the files it needed, a multi-hop chain cites every file in the chain, and withdrawn revisions are ignored. Each project is also a reusable tool with a CLI, a Python API and a local Studio web UI. On the public samples, using AMD Token Lab's hosted Qwen3.8-27B, Lumen OCR scored 10/10 and Provenance RAG 10/10. I have not yet run the shipped 8B model on an AMD GPU. Match 4 image: docker.io/abuhuraira0001/alx-workbench:m4-v1 Code: github.com/abuhuraira99/wayfinder, github.com/abuhuraira99/lumen-ocr, github.com/abuhuraira99/provenance-rag

Abu Huraira

An agent triages an untrusted package before install: it plans probes, runs them in a hardened container on Vultr, and returns a verdict backed by the egress proxy log, the process result and a filesystem diff the package cannot write.

Kosi IT

DryRun AI analyzes Python code, executes it inside a restricted Docker sandbox on Vultr, and compares AI-generated predictions with recorded execution evidence in a structured report.

DryRun AI

Find the smallest generated input that breaks your code, then replay it in a fresh sandbox. Vultr Serverless Inference builds the oracle and tests; gVisor isolates execution on a Vultr VM.

Counterproof