Travis Muhlestein PRO
TravisMuhlestein
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all AI & ML Interests
Recent Activity
posted
an
update
5 days ago
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.
posted
an
update
7 days ago
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.
posted
an
update
28 days ago
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/
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