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https://www.godaddy.com/resources/engineering

AI & ML interests

Product & AI CTO at GoDaddy focused on AI infrastructure, orchestration, agent systems, observability, and enterprise-scale AI deployment

Recent Activity

posted an update 7 days ago
I've been interested in how coding agents handle tasks beyond generating code. Deployment is a useful example: it involves interacting with a real environment, following a sequence of steps, and checking that the result actually works. The GoDaddy Node.js hosting workflow supports deployment through its CLI, including workflows that can be driven by a coding agent. The walkthrough covers the setup and deployment process: https://www.godaddy.com/resources/news/deploy-a-node-js-app-to-godaddy-from-your-terminal-or-let-your-coding-agent-do-it Curious how others are handling deployment in agent-driven workflows. Which steps do you let the agent execute, and which do you keep under direct human control? #CodingAgents #NodeJS
posted an update about 1 month ago
As AI agents become more autonomous, identity becomes part of the infrastructure problem. We’ve been working on Agent Name Service (ANS) to give agents a verifiable identity before they interact or exchange sensitive information. One design goal is that verification shouldn’t depend on a centralized service for every request. ANS can verify an agent offline against published root keys, with the verification path completing in under 0.5 ms on a single server core. The work builds on infrastructure we already have around domains and DNS. We’re also looking at that infrastructure at the DNS layer through SVCB and HTTPS records, evolving how modern applications connect beyond traditional CNAME-based architectures. Two engineering deep dives: → Don’t Trust. Verify. Offline, Sub-Millisecond Agent Verification with ANS https://www.godaddy.com/resources/news/dont-trust-verify-offline-sub-millisecond-agent-verification-with-ans → Beyond CNAME Flattening https://www.godaddy.com/resources/news/beyond-cname-flattening
repliedto their post about 2 months ago
One of the most underrated parts of AI-assisted engineering may have nothing to do with the model. It's the infrastructure around the engineer. Shriyash Balshetwar and Shubham Jangle, engineers at GoDaddy, were spending roughly 16 mechanical pull requests per week managing localization branches. None of the work was particularly difficult—it was repetitive, easy to forget, and occasionally capable of blocking releases. They built a GitHub App to remove the entire workflow. What I find interesting is what happened underneath the simple idea of "automate the PRs." The production system needed idempotent webhook handling, installation-scoped authentication, retry logic around GitHub's asynchronous mergeability state, per-repository configuration, and safe pattern matching. The app itself is under 900 lines of JavaScript. The interesting lesson is that the hard part of automation isn't always the automation. It's building the reliability around it. As AI agents take on more software engineering tasks, I expect this distinction to become even more important. The systems surrounding an agent—events, permissions, state, retries, validation, and feedback—may matter as much as the model making the decision. 🔗 https://www.godaddy.com/resources/news/how-a-github-app-saved-us-hours-of-manual-effort Curious what other engineering workflows people have found worth automating end-to-end.
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