AI Engineer session on Your Support Team Should Ship Code, presented by Lisa Orr, Zapier. It adds practical context for how teams are building and operating AI systems in production.
More curated notes connected through AI Engineering.
Microsoft’s FORGE account describes the work between a model’s vulnerability claim and a useful repair: reusable builds, duplicate removal, reachability checks, project-specific verification, reproducible triggers and regression tests. Structured rejection reasons help improve later searches. The useful operational measure is the flow of findings that survive verification and reach a fix, rather than the number of candidates generated. Reported successful-case costs exclude parts of screening, failed attempts and human work, so they are not the total cost of operating this pipeline.
OpenAI describes a staged program for AI-assisted defense: use agents to review code and infrastructure, triage alerts, enumerate attack paths, and validate security invariants while retaining strong isolation and least privilege. Its recommended rollout starts with internet-facing services and vulnerability backlogs, moves security review into CI, requires focused fixes and regression tests, and expands from read-only triage to narrowly bounded automation only after teams build evidence and confidence.
Google opened a preview of CodeMender, an AI code-security agent delivered through Gemini Enterprise Agent Platform and AI Threat Defense. It is designed to inspect code, identify and validate potentially exploitable defects, and produce targeted fixes, with Google’s specialized Gemini 3.5 Flash Cyber model initially restricted to governments and trusted partners.