At GoDaddy, our engineering team has been experimenting with how to make AI agents more modular, auditable, and production-ready. Instead of treating agents as monoliths, we designed them around four core tools that separate decision from execution:
* MemoryTool – maintains persistent context and user continuity
* CompletionTool – defines task completion and termination criteria
* UserInteractionTool – handles clarifications, approvals, and feedback loops
* DelegationTool – enables handoffs between agents or humans
This approach makes agent behavior transparent, versionable, and safer to scale. We’ve open-sourced our thinking and early framework notes in a technical write-up here:
Would love to hear feedback from others working on modular or composable agent architectures — especially how you handle agent memory persistence and versioning.
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[ 2.9 ms ] story [ 15.0 ms ] thread* MemoryTool – maintains persistent context and user continuity
* CompletionTool – defines task completion and termination criteria
* UserInteractionTool – handles clarifications, approvals, and feedback loops
* DelegationTool – enables handoffs between agents or humans
This approach makes agent behavior transparent, versionable, and safer to scale. We’ve open-sourced our thinking and early framework notes in a technical write-up here:
Building AI Agents at GoDaddy – An Agent’s Toolkit https://www.godaddy.com/resources/news/building-ai-agents-at...
Would love to hear feedback from others working on modular or composable agent architectures — especially how you handle agent memory persistence and versioning.