Building AI Agents from First Principles at GoDaddy
Everyone’s talking about AI agents lately, and for good reason. But at GoDaddy, we’re going deeper: starting from first principles to explore what makes an agent truly robust and usable in real-world scenarios.
Instead of asking “What can we build fast?” we’re asking “What design choices make agents flexible, testable, and reliable long term?”
Core Concepts
• Tool-centric design: everything an agent does is a tool call, with precise APIs and granularity.
• Decision vs. delivery: agents decide what to do; tools handle how to do it—keeping systems modular.
• Structured outputs & reflection: LLMs output both the tool call and the reason behind it, making debugging and iteration easier.
• Universal tools: even user interactions (inform, confirm, request) are abstracted as tools, clarifying boundaries between logic and interface.
Real-world use cases → Not just theory
• Routing and responding to support messages
• Surfacing emerging trends in sales data
• Automating scheduling, inventory, or operations orchestration
What we learned
• Treating everything as a tool makes systems more predictable and extensible
• LLM “verbosity” is valuable—it reveals reasoning and speeds iteration
• Separating decision from execution reduces fragility and simplifies updates
We’re still at the beginning, but these principles give us a strong foundation. As agents evolve, architectural clarity matters more than chasing the latest framework.
Building AI Agents from First Principles at GoDaddy
Everyone’s talking about AI agents lately, and for good reason. But at GoDaddy, we’re going deeper: starting from first principles to explore what makes an agent truly robust and usable in real-world scenarios.
Instead of asking “What can we build fast?” we’re asking “What design choices make agents flexible, testable, and reliable long term?”
Core Concepts
• Tool-centric design: everything an agent does is a tool call, with precise APIs and granularity. • Decision vs. delivery: agents decide what to do; tools handle how to do it—keeping systems modular. • Structured outputs & reflection: LLMs output both the tool call and the reason behind it, making debugging and iteration easier. • Universal tools: even user interactions (inform, confirm, request) are abstracted as tools, clarifying boundaries between logic and interface.
Real-world use cases → Not just theory
• Routing and responding to support messages • Surfacing emerging trends in sales data • Automating scheduling, inventory, or operations orchestration
What we learned
• Treating everything as a tool makes systems more predictable and extensible • LLM “verbosity” is valuable—it reveals reasoning and speeds iteration • Separating decision from execution reduces fragility and simplifies updates
We’re still at the beginning, but these principles give us a strong foundation. As agents evolve, architectural clarity matters more than chasing the latest framework.
Curious about architecture patterns that scale? Dive in here: Building AI Agents at GoDaddy: An Experiment in First Principles https://www.godaddy.com/resources/news/building-ai-agents-at...