Travis Muhlestein PRO
TravisMuhlestein
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posted an
update
1 day ago
AI coding tools are changing engineering — not replacing engineers
There’s a lot of conversation right now about whether AI coding tools will replace software engineers.
In practice, what many teams are experiencing is a shift in where the complexity lives.
AI can generate code surprisingly well.
But building production systems still requires engineers to handle problems like:
-system architecture and abstractions
-integration between services and models
-failure modes and observability
-scaling infrastructure and data pipelines
-deciding what automation should (or shouldn’t) do
One interesting side effect of AI coding tools is that engineers increasingly start automating their own routine workflows, which lets them focus on the bigger architectural and system-level challenges.
Less time writing boilerplate. More time designing systems that safely integrate AI capabilities.
Interesting perspective here: https://www.godaddy.com/resources/news/dear-software-engineer-you-still-have-value
Curious how others here see engineering roles evolving as AI tools improve. posted an
update
8 days ago
Moving AI from experiments to production systems (GoDaddy + AWS case study)
A recurring pattern across many organizations right now is that AI experimentation is easy — operationalizing it is much harder.
This case study from AWS describes how GoDaddy has been deploying AI systems in production environments using AWS infrastructure.
One example is Lighthouse, a generative AI system built using Amazon Bedrock that analyzes large volumes of customer support interactions to identify patterns, insights, and opportunities for improvement.
The interesting part isn’t just the model usage — it’s the system design around it:
- large-scale interaction data ingestion
- LLM-driven analysis pipelines
- recursive learning platforms where real-world signals improve systems over time
- infrastructure designed for continuous iteration
We’re starting to see a shift where organizations move from AI prototypes toward AI platforms and production systems.
Would be interested to hear how others in the community are thinking about:
- production AI architectures
- LLM evaluation pipelines
- Feedback loops in real-world systems
- infrastructure for scaling AI workloads
Case study:
https://aws.amazon.com/partners/success/godaddy-agenticai/
posted an
update
22 days ago
Publishing AI Agent Identity to Public DNS: GoDaddy ANS + MuleSoft Agent Fabric
As AI agents move into production systems, one issue keeps resurfacing: identity.
Not model quality.
Not orchestration.
Identity.
GoDaddy’s Agent Name Service (ANS) registers AI agents and publishes their identity to the public DNS, binding them to domain ownership and cryptographic proof.
With the new integration between ANS and Salesforce’s MuleSoft Agent Fabric:
-Verified agents can be pulled into MuleSoft’s enterprise registry
-Teams can inspect verification status and publisher metadata
-Policies can be applied before agents access APIs and data
What’s interesting here is architectural separation:
-ANS → global identity + verification signal
-Agent Fabric → enterprise governance + orchestration
No closed directory requirement.
No proprietary lookup layer.
Identity becomes DNS addressable.
This feels like an early step toward treating agent identity as a public infrastructure primitive — like how TLS certificates enabled trusted HTTPS.
Curious how others in the HF community are thinking about:
-Agent identity standards
-DNS-based verification
-Interoperability across agent frameworks
More:
-www.godaddy.com/ans
-https://www.mulesoft.com/ai/agent-fabric
-https://aboutus.godaddy.net/newsroom/news-releases/press-release-details/2026/GoDaddy-ANS-Integrates-with-Salesforces-MuleSoft-Agent-Fabric/default.aspx
-https://blogs.mulesoft.com/news/mulesoft-agent-fabric-godaddy-ans-for-agent-discovery-and-verification/ Organizations
