Hi Google AI community,
I am exploring approaches for building reliable AI agent applications using Gemini models.
While creating AI-powered applications is becoming easier, moving from prototypes to production introduces challenges around reliability, context management, and workflow execution.
I would like to learn from developers building real-world Gemini applications:
- How are you designing agent workflows with Gemini models?
- What patterns work best for managing context and conversation memory?
- How are you handling tool calling and external API integrations?
- What approaches are you using to evaluate and improve agent responses?
- How do you manage reliability when AI outputs influence automated workflows?
I am interested in learning about practical architectures and lessons learned from production AI applications.
Looking forward to hearing from the community.