InfoQ - AI Harness

Google Rewrites Critical C Dependencies to Rust Using AI and Differential Fuzzing

Olimpiu Pop — Sun, 27 Sep 2026 14:14:00 GMT

Google's security team developed a method to replace legacy C code in the giflib image-processing library with Rust, targeting inherent memory vulnerabilities. They utilised an automated migration process, ensuring compatibility and zero-day vulnerability mitigation. The project successfully maintained runtime performance while demonstrating that AI translations require ongoing human oversight.

By Olimpiu Pop

Article: The Agent Harness: What it is and Two Ways to Build One

Trista Pan — Fri, 25 Sep 2026 09:00:00 GMT

This article explains the development and operational layers of an agent harness through two implementations of a finance assistant: AWS AgentCore Harness and LangChain with Envoy AI Gateway. It compares how each handles tools, memory, model access, cost control, and observability, and examines the trade-offs in operational ownership, portability, and engineering effort.

By Trista Pan

Presentation: The Agent Harness: Control Planes, Invariants, and Approval Boundaries for Production AI Agents

Vinoth Govindarajan — Mon, 21 Sep 2026 11:00:00 GMT

OpenAI’s Vinoth Govindarajan discusses why production AI agents fail beyond model hallucination. Using real-world case studies like OpenClaw, he explains the key principles of reliable agent harnesses: establishing explicit state ownership, serializing concurrent state mutations, scoping execution authority, and validating actions at the user-visible edge.

By Vinoth Govindarajan