Hi Google AI community,
I have been exploring agentic development approaches and how AI agents can assist developers beyond simple code generation.
As AI agents become more capable, I am interested in understanding the best practices for building reliable agent-based workflows.
Some areas I am exploring:
- Planning and task decomposition by AI agents
- Connecting agents with external tools and APIs
- Managing agent memory and context
- Handling errors and unexpected outputs
- Evaluating agent performance in real-world scenarios
For developers experimenting with agentic applications:
- What architecture patterns are you finding effective for AI agents?
- How do you handle validation before an agent performs important actions?
- What challenges have you faced when moving from simple AI assistants to autonomous workflows?
Would love to hear practical experiences and lessons learned from the community.