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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-godaddy-an-experiment-in-first-principles Organizations
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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-godaddy-an-experiment-in-first-principles
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-godaddy-an-experiment-in-first-principles
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Reachy Mini - The Open-Source Robot for Today's and Tomorrow's AI Builders
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