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Securing AI agents at the scale of the internet
TravisMuhlestein
• • 1Travis Muhlestein PRO
TravisMuhlestein
AI & ML interests
Product & AI CTO at GoDaddy focused on AI infrastructure, orchestration, agent systems, observability, and enterprise-scale AI deployment
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posted an update 1 day ago
AI orchestration > bigger models?
Over the past few months I've been experimenting with a different approach to AI-assisted creation.
Instead of asking one frontier model to do everything, I explored what happens when specialized capabilities are orchestrated into a single production workflow.
The project coordinates drafting, editing, illustration, multilingual translation, narration, publishing outputs, and marketing assets into one end-to-end system.
The first real-world validation is The Isaiah Chronicles, the first release in a planned 10-book series.
Over roughly two months the workflow processed more than 14 billion tokens, with continuous experimentation around evaluation metrics, quality gates, reproducibility, and orchestration strategies.
One observation kept repeating itself:
Improving the orchestration often produced bigger gains than changing the underlying model.
I'm curious whether others building AI systems are seeing the same pattern.
How much of the next leap comes from better models—and how much comes from better orchestration?
🔗 https://theisaiahchronicles.com posted an update 6 days ago
Developer platforms are entering their biggest redesign in decades.
Not because APIs are changing. Because the consumers of those platforms are changing.
For decades, developer platforms have assumed a human would read the documentation, authenticate, write the integration, interpret errors, and decide what to do next.
AI agents change that assumption.
They're becoming autonomous consumers of developer platforms—not just of individual endpoints.
That affects far more than API design. Authentication must support agents acting on behalf of developers.
Documentation needs to ground LLMs, not just educate humans.
Capability discovery needs to be machine-readable at the platform level.
SDKs need to support autonomous workflows, not just developer productivity.
These aren't API design problems anymore.
They're platform design problems.
That's what I find interesting about the new GoDaddy Developer Platform.
It isn't simply exposing Domain APIs.
It's rethinking the platform underneath them so developers and AI agents become equal participants in modern software development.
I think we'll see many more developer platforms evolve in this direction over the next few years.
🔗 https://www.godaddy.com/resources/news/introducing-the-godaddy-developer-platform-domain-apis-for-developers-and-their-agents
Curious how others see this evolving.
Should developer platforms explicitly optimize for AI agents—or should agents simply become better at adapting to today's platforms? posted an update 14 days 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. Organizations
published an article 10 months ago