SkCC: Portable and Secure Skill Compilation for Cross-Framework LLM Agents
Paper • 2605.03353 • Published • 7 •
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Travis Muhlestein PRO
TravisMuhlestein
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 about 7 hours ago
One of the more interesting questions emerging around AI-assisted software engineering isn't whether models can write code.
We've already crossed that threshold.
The harder question is:
Which programming languages are best suited for AI-generated software?
One lesson we've seen at GoDaddy is that AI-assisted development changes what we optimize for. It's no longer enough to generate code quickly—we also need systems that help AI continuously verify, refine, and improve what they produce.
I'm a huge Rust fan. It's extremely durable, and its lints and chatty compiler are exactly the kind of feedback loops LLMs devour. We're seeing systems running at one-tenth the cost with 10x the durability, all built through AI test-driven development with full documentation and backward/forward compatibility.
Rust offers an interesting answer.
Its ownership model, strict compiler, and memory safety guarantees create an environment where verification becomes part of the development loop rather than something added afterward.
For autonomous coding agents, that's a meaningful shift.
Instead of relying primarily on runtime failures or manual review, agents receive immediate compiler feedback that improves subsequent iterations. The compiler effectively becomes another participant in the reasoning loop.
This shifts the conversation beyond code generation.
It's about building engineering systems where generation, verification, and correction happen continuously.
As agentic development evolves, language design may become just as important as model capability.
Great technical write-up from the GoDaddy Engineering team.
🔗 https://www.godaddy.com/resources/news/rust-is-the-native-language-of-agentic-development
Curious whether others think AI-native development will influence programming language adoption—or whether models will simply adapt to the languages developers already use. posted an update 15 days ago
The conversation around AI agents is evolving.
We're moving beyond model capabilities and toward the infrastructure needed for agents to work together.
Over the past few weeks we've seen meaningful momentum around the foundational building blocks of the emerging agentic web.
Agent Name Service (ANS) is addressing identity and trust.
Agentic Resource Discovery (ARD) is helping standardize how agents discover resources and capabilities.
Together, these efforts represent something bigger than individual projects.
They point toward an ecosystem built on open, interoperable infrastructure rather than isolated implementations.
As builders, we'll likely spend the next few years solving challenges around identity, discovery, trust, interoperability, and governance—not just model performance.
It will be interesting to see how these efforts evolve—and where the community chooses to collaborate next.
Learn more:
🔗 Linux Foundation ANS: https://www.linuxfoundation.org/press/linux-foundation-announces-intent-to-launch-agent-name-service-to-establish-trusted-identity-infrastructure-for-ai-agents
🔗 Agentic Resource Discovery: https://developers.googleblog.com/announcing-the-agentic-resource-discovery-specification/ posted an update 17 days ago
One of the less-discussed applications of AI is data governance at scale.
At GoDaddy, we manage thousands of datasets, with hundreds requiring elevated governance and certification. The traditional process—gathering evidence across multiple systems, validating controls, and preparing reviews—was becoming increasingly difficult to scale.
We built TrustTier, an AI governance agent designed to support the certification lifecycle.
The interesting challenge wasn't automation. It was judgment.
The system reasons across three states:
- Assigned tier — the classification currently approved in systems of record
- Intended tier — the classification requested by the data owner
- Qualified tier — the classification supported by available evidence
That distinction matters because governance isn't simply about retrieving information. It's about determining whether the evidence justifies a decision and clearly explaining why.
The same certification logic can then be reused across verification, review, and audit workflows.
Curious how others are approaching explainability, governance, and false-positive management in AI-assisted compliance systems.
🔗 https://www.godaddy.com/resources/news/from-manual-audits-to-intelligent-certification Organizations
