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TravisMuhlestein  updated a Space 28 days ago
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TravisMuhlestein  published a Space about 1 month ago
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TravisMuhlestein 
posted an update 10 days ago
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155
What if a “team” is just a set of agents?

We tend to think of agents as tools that help engineers.
But this example flips that model.

A set of prompt-native agents is handling security workflows that would normally involve multiple roles — without relying on traditional codebases.

Instead of: engineer → tool → output
It becomes: agent → execution → continuous operation

That changes how you think about systems:

No explicit task queues
No role-based handoffs
Minimal code as the coordination layer

The system is defined more by prompts and interactions than by code structure.

This raises interesting questions:

Where do you enforce correctness in a system like this?
How do you debug behavior without traditional code paths?
What replaces “ownership” when the work is distributed across agents?

🔗 https://www.godaddy.com/resources/news/the-zero-code-security-team-shifting-left-with-prompt-native-ai-agents
TravisMuhlestein 
posted an update 15 days ago
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87
From AI Outputs to AI Execution: Why Identity Comes First


AI agents are moving beyond the “safe zone” of generating drafts, recommendations, and insights that require human action.

With systems like Salesforce Agent Fabric and MuleSoft, agents can now execute directly inside enterprise environments — calling APIs, updating records, and triggering workflows.

This shift changes the problem.

It’s no longer about evaluating outputs.
It’s about controlling execution.

Once agents can act, identity becomes critical — not just to verify who an agent is, but to determine what it is allowed to do before any action takes place.

This is where Agent Name Service (ANS) comes into play.

ANS moves identity to the front of the decision layer, enabling systems to establish permissions and trust boundaries prior to execution.

In an agentic architecture, trust is not something you validate after the fact — it’s a prerequisite to action.

More details on how this integrates with MuleSoft:
https://blogs.mulesoft.com/news/verify-agent-identity-mulesoft-godaddy-ans/
TravisMuhlestein 
posted an update 23 days ago
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119
What happens after agent deployment becomes easy?


As platforms like Salesforce’s Agent Fabric reduce the friction of building and orchestrating agents, a new pattern starts to emerge.

The complexity doesn’t stop at creation. It starts after deployment.

Once agents are distributed across systems, APIs, and workflows, they effectively become part of a larger system.

That introduces new challenges:

- Tracking where agents are operating
- Understanding behavior across environments
- Managing interactions between agents and systems
Maintaining consistency at scale

In that sense, agent pipelines don’t just create agents.

They create distributed systems.

And those systems require a different level of visibility and coordination than isolated tools.

Open questions:

- How do we observe agents once they’re deployed?
- How do we manage behavior across systems?
- What does control look like post-deployment?

🔗 https://www.salesforce.com/news/stories/agent-fabric-control-plane-announcement/
TravisMuhlestein 
updated a Space 28 days ago
TravisMuhlestein 
posted an update about 1 month ago
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151
We’re starting to see a shift: From building individual agents → to thinking about agent ecosystems.

And that raises a different set of problems.

Not just: “what can my agent do?” But: “how does my agent exist and interact in a broader network?”

Because once agents operate outside a single app or framework, they need:

- A way to be discovered
- A way to prove who they are
- A way to interact with other agents across environments

That’s where open infrastructure becomes critical.

The collaboration between Cloudflare and GoDaddy is interesting because it points toward an agentic web that’s:

- Not tied to a single provider
- Built on shared primitives
- And compatible across systems

If you’re building agents today, this is the bigger question to keep in mind:

Are we creating isolated capabilities — or contributing to a network that can actually scale?

🔗 https://www.cloudflare.com/press/press-releases/2026/cloudflare-and-godaddy-partner-to-help-enable-an-open-agentic-web/
TravisMuhlestein 
published a Space about 1 month ago
TravisMuhlestein 
posted an update about 1 month ago
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179
A real-world example of agent identity and trust (GoDaddy x LegalZoom)

As agent-based systems start to operate across ecosystems, one challenge becomes increasingly visible: identity.

Most discussions focus on what agents can do, but less on how systems verify who they are interacting with.

This partnership between GoDaddy and LegalZoom is an interesting real-world example.

LegalZoom published its AI agent using ANS (Agent Name Service), which binds the agent to a domain-based identity and provides cryptographic verification.

This allows systems to:

-Confirm agent origin
-Verify authenticity before execution
-Establish trust across interactions

Architecturally, this suggests a shift:

Identity is moving from being implicit → to becoming part of the infrastructure layer.

Similar to how DNS and TLS enabled trusted communication on the web, agent ecosystems may require built-in primitives for identity and verification.

Curious how others are approaching:

-Identity layers for agents
-Verification mechanisms in production
-Trust in cross-agent interactions

🔗 https://aboutus.godaddy.net/newsroom/news-releases/press-release-details/2026/GoDaddy-and-LegalZoom-Partner-to-Support-Open-Agentic-Web/default.aspx
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TravisMuhlestein 
posted an update about 1 month ago
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408
Routing and trust are becoming coupled problems in multi-agent systems

As agent-based systems scale, two challenges start to converge: routing and trust.

Routing determines which agent should act. As the number of specialized agents increases, selecting the right one efficiently becomes non-trivial.But selecting an agent is only part of the problem.

In production systems, you also need to verify who that agent is before allowing it to execute. Without identity and verification, routing decisions are made on components that may not be trustworthy.

This creates an interesting architectural split:

-routing → decides what gets executed
-identity → determines whether it should be trusted

GoDaddy’s ANS (Agent Name Service) introduces a model where agents are tied to domain-based identity and can be cryptographically verified before interaction.

This suggests a shift where identity becomes part of the underlying infrastructure, similar to how DNS and TLS evolved for the web.

Curious how others are thinking about:

-routing strategies (static vs dynamic vs learned)
-identity layers for agents
-verification and trust in production systems

🔗 https://www.godaddy.com/resources/news/intelligent-ai-routing
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TravisMuhlestein 
posted an update about 2 months ago
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125
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.
TravisMuhlestein 
posted an update 2 months ago
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2238
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/

TravisMuhlestein 
posted an update 3 months ago
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194
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/
TravisMuhlestein 
posted an update 4 months ago
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228
Designing an acquisition agent around intent and constraints

We recently shared how we built an acquisition agent for GoDaddy Auctions, and one thing stood out: autonomy is easy to add—intent is not.

Rather than optimizing for agent capability, the design centered on:

-making user intent explicit and machine-actionable
-defining clear constraints on when and how the agent can act
-integrating tightly with existing systems, data, and trust boundaries

In our experience, this framing matters more than model choice once agents move into production environments.

The article describes how we approached this and what we learned when intent and constraints became core architectural inputs.

Link:
https://www.godaddy.com/resources/news/godaddy-auctions-building-the-acquisition-agent

Would love to hear how others here think about intent representation and guardrails in agentic systems.
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TravisMuhlestein 
posted an update 4 months ago
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2449
Agentic AI doesn’t fail because it lacks intelligence — it fails because it lacks context.

As agents become more autonomous, the real challenge shifts from generation to governance:
understanding when, why, and under what constraints an agent should act.

At GoDaddy, we’ve been treating context as a first-class primitive for agentic systems —
combining identity, intent, permissions, and environment so agents can operate responsibly in production.

Context is what turns automation into judgment.
Without it, autonomy becomes risk.

This post outlines how we’re thinking about the transition from task execution to context-aware agentic systems, and what that means for building AI that can be trusted at scale.

👉 How we build context for agentic AI:
https://www.godaddy.com/resources/news/how-godaddy-builds-context-for-agentic-ai

Curious how others here are modeling context, trust boundaries, and decision constraints in agentic architectures.
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TravisMuhlestein 
posted an update 5 months ago
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244
From AI demos to production systems: what breaks when agents become autonomous?

A recurring lesson from production AI deployments is that most failures are system failures, not model failures.

As organizations move beyond pilots, challenges increasingly shift toward:

• Agent identity and permissioning
• Trust boundaries between agents and human operators
• Governance and auditability for autonomous actions
• Security treated as a first-class architectural constraint

This recent Fortune article highlights how enterprises are navigating that transition, including work with AWS’s AI Innovation Lab.

Open question for the community:
What architectural patterns or tooling are proving effective for managing identity, permissions, and safety in autonomous or semi-autonomous agent systems in production?

Context: https://fortune.com/2025/12/19/amazon-aws-innovation-lab-aiq/
TravisMuhlestein 
posted an update 5 months ago
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211
Calibrating LLM-as-a-Judge: Why Evaluation Needs to Evolve

As AI systems become more agentic and interconnected, evaluation is turning into one of the most important layers of the stack. At GoDaddy, we’ve been studying how LLMs behave when used as evaluators—not generators—and what it takes to trust their judgments.

A few highlights from our latest engineering write-up:

🔹 Raw LLM scores drift and disagree, even on identical inputs
🔹 Calibration curves help stabilize model scoring behavior
🔹 Multi-model consensus reduces single-model bias and variance
🔹 These techniques support safer agent-to-agent decision making and strengthen our broader trust infrastructure (ANS, agentic systems, etc.)

If you're building agents, autonomous systems, or any pipeline that relies on “AI judging AI,” calibration isn’t optional — it's foundational.

👉 Full write-up: Calibrating Scores of LLM-as-a-Judge
https://www.godaddy.com/resources/news/calibrating-scores-of-llm-as-a-judge

Would love feedback from the HF community:
How are you calibrating or benchmarking model evaluators in your own workflows?
TravisMuhlestein 
posted an update 6 months ago
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193
🚀 GoDaddy ANS API Now Live — Bringing Verifiable Identity to the Agent Ecosystem

We just launched the Agent Name Service (ANS) API) publicly, along with the new ANS Standards site, extending decades of GoDaddy internet-scale trust into the emerging world of autonomous agents. ANS provides cryptographically verifiable identity, human-readable names, and policy metadata for agents — designed to work across frameworks like A2A, MCP, and future agent protocols.

What’s new:

🔹ANS API is open to all developers — generate a GoDaddy API key and start testing registration, discovery, and lifecycle ops.
🔹ANS Standards Site is live — includes the latest spec, architecture, and implementation guidance.
🔹Protocol-agnostic adapter layer — supports interoperability without vendor lock-in.

Why it matters:

As autonomous agents continue to proliferate, we need neutral, verifiable identity to prevent spoofing, trust rot, and fragmented ecosystems. ANS brings DNS-like discovery and PKI-based validation to the agent economy.

🔗 Links

Standards & docs: https://www.agentnameregistry.org/
API keys: https://developer.godaddy.com/keys
Repo: https://github.com/godaddy/ans-registry
PR: https://aboutus.godaddy.net/newsroom/news-releases/press-release-details/2025/GoDaddy-advances-trusted-AI-agent-identity-with-ANS-API-and-Standards-site/default.aspx

Would love to hear thoughts from the community:
What should a universal agent identity layer guarantee — and what should it avoid?
TravisMuhlestein 
posted an update 6 months ago
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1410
Building Smarter AI Agents: A Tool-Based Architecture for Modularity and Trust

Over the past year, our AI engineering team at GoDaddy has been rethinking how to make agent systems more modular, transparent, and production-ready. Instead of viewing an AI agent as a monolithic process, we’ve decomposed it into four core tools that separate decision-making from execution — a design that’s proving critical for scale and observability:

🧩 MemoryTool – maintains persistent context and user continuity
✅ CompletionTool – determines when a task is truly complete
💬 UserInteractionTool – manages clarifications, approvals, and confirmations
🔁 DelegationTool – enables agents to hand off tasks to other agents or humans

This approach makes every step of an agent’s workflow explicit, testable, and auditable, allowing us to scale AI systems in production with higher confidence. We see this as a step toward a more open, composable agent ecosystem — one where frameworks can interoperate and agents can build trust through transparency and version control.

Read the full write-up here → Building AI Agents at GoDaddy – An Agent’s Toolkit https://www.godaddy.com/resources/news/building-ai-agents-at-godaddy-an-agents-toolkit

We’d love to collaborate and exchange ideas with the community:

- How are you designing modular agent architectures?
- What design patterns or abstractions have helped you manage agent complexity?

Let’s build smarter, safer agents together.

#AI #Agents #Architecture #MachineLearning #OpenSource #AgentFrameworks #TrustInAI
TravisMuhlestein 
posted an update 6 months ago
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The Next Step in AI: Trust That Scales with Autonomy

The past few months have proven that autonomous agents are testing the web’s guardrails, from robots.txt to checkout flows, and trust is the casualty. At GoDaddy, we’re addressing this by supporting the Agent Name Service (ANS) open standard — a framework introducing a digital passport for autonomous agents. By binding each agent’s identity to its exact version of code, ANS creates verifiable trust even in multi-agent chains, reducing the risks of compromised code or “trust rot.”

This is more than a technical milestone. It’s the foundation for secure, scalable agent-to-agent commerce. This post follows our announcement from last month: ANS is now live, and we’re partnering with select companies to roll out implementations in the near future.

Exciting times ahead as we continue shaping the agent economy.

👉 Read more in Scott Courtney’s latest post: Why the Agent Economy Needs a Digital Passport https://www.godaddy.com/resources/news/why-the-agent-economy-needs-a-digital-passport

💬 What do you think: could verifiable, version-bound identity be the missing piece for safe autonomous collaboration?
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TravisMuhlestein 
posted an update 6 months ago
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213
Balancing Revenue and Relevance: Deep Learning Meets Domain Ads

In most ad systems, the logic is simple: highest bid wins. But simple doesn’t mean smart. At GoDaddy, we asked a harder question: Can we optimize both revenue and user satisfaction—without sacrificing either?

So, we built DeepAd: a deep learning ranking model that blends monetization, behavior, and intent to deliver ads that are both profitable and meaningful.

Core Concepts

• Context-aware ranking: Goes beyond “highest bid wins,” factoring in intent, engagement, and diversity.
• Transfer learning from DeepRank: Leveraging billions of domain-search interactions to give our ad model a head start.
• ReTiRe framework: Balancing Relevance, Time, and Revenue for a truly adaptive signal mix.
• Real behavioral supervision: Learning directly from shopper actions — what they clicked, bought, or skipped.

Why this matters:

✅ Shoppers see domains that actually fit their intent.
✅ Registries get fair exposure — niche top-level domain (TLD) can compete on merit, not budget.
✅ GoDaddy achieves sustainable revenue without degrading user trust.

Key takeaway: Building intelligent ad systems isn’t just about monetization — it’s about aligning incentives between users, advertisers, and platforms through smarter modeling.

👉 Dive into the technical deep dive: Balancing Revenue and Relevance: Optimizing TLD Ad Rankings https://www.godaddy.com/resources/news/balancing-revenue-and-relevance-optimizing-tld-ad-rankings

Curious to hear your perspective — share your thoughts below!

godaddy
TravisMuhlestein 
posted an update 6 months ago
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1335
Smarter AI Starts with Consensus

The next frontier in AI isn’t about bigger models — it’s about smarter collaboration.

At GoDaddy, we’re exploring Consensus of LLMs (CoL) — an orchestration approach that brings multiple Large Language Models together to reach agreement before producing an answer.

By combining diverse perspectives from different models — GPT, Claude, Gemini, Llama, and others — CoL helps us:

✅ Reduce hallucinations in AI-generated outputs
✅ Increase factual accuracy and transparency
✅ Make AI safer for real-world, high-stakes business decisions

From customer support bots to security guidance and website content creation, this approach is making AI more reliable, explainable, and aligned with customer trust.

Read more from Jay Gowdy on how GoDaddy is pioneering trustworthy AI at scale 👇
👉 Consensus of LLMs: Scaling Accuracy Beyond a Single Model https://www.godaddy.com/resources/news/consensus-of-llms-scaling-accuracy-beyond-a-single-model

💬 Curious to hear from the AI community: could consensus between models be the key to more trustworthy systems? Share your thoughts — we’d love to continue the conversation.

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