This is a submission for the Google AI Agents Writing Challenge: Learning Reflections
That's a wrap! Thank you for Kaggle and Google offer such a valuable learning resources, and make it accessible for all!
Overall Experiences
This was my second time participating AI Agent development courses on Kaggle/Google.
While I enjoy both courses - especially the energy (!), the AI Agent Intensive course stands out for a few reasons IMO:
- ADK ecosystem and product suite maturity. The comprehensiveness and enhanced features demonstrate the evolution and progress Google ADK has made in the past 6 months.
The synergy with Google Cloud Platform, particularly, Vertex AI and CloudRun / Cloud Logging / Tracing, makes ADK a compelling option for developers.
- E2E production readiness. Related to above, this course showcase multi-agents system design pattens - sequential, parallel, loop-based, as well as E2E development-to-deployment-observability & evaluation workflow.
It is a major milestone signaling that Google Gemini / ADK transitioning from prototyping to production-ready stage.
- Last not least. The format - combining white paper, lecture and hands-on code example, strengthens the knowledge retention. And just more fun to learn!
Key Takeaways
My main takeaway from this course and my own experiences is that, personalization powered by smart memory and session retention, more aptly curation, defines the success of AI applications, more so than the LLM model itself.
Magics happens when AI agents becomes anticipatory, beyond simply automating workflow. It can only do so if it can 'remember' who the users are, what they prefer and how they work.
To push it further, when the memory becomes innate to the agent / LLM in-use, switching cost increases significantly. Memory == Product Stickiness.
Naturally, the session that I found especially applicable and inspirational for my own project was 'Session and Memory Management'. It is mainly trade-off management, to strike balance between performance (latency, accuracy), users experiences and cost (token, storage).
Features like ContextCacheConfig, SessionService and MemoryService and their combined application and adaptation are a sequence of architectural decisions that every developers / agent-builders will have to carefully evaluate when the application goes from ideation to production.
In my own project, a personal assistant for email digest, converting inbox emails to a listening experiences for users on-the-go, latency issue was a major pain point. The other was excessive logging issue. To address the latency issue, I had implemented semantic caching to fast-track time-to-first-token for commonly used queries. So that it shorten text-generation and better user experiences. To minimize memory storage, I leveraged to dynamically control logging (memory management) details. However, I believe there are plenty room to improve as I will be continue expanding the capability of the agent goes beyond emails as input.
There are other extraneous factors that have to be taken into consideration once beyond prototyping - privacy and data protection. On one hand, logging the full sessions and user conversation provides a rich context for downstream analysis and evaluation, informing future improvement. On the other hand, session/memory retention creates significant liability and risk for data and privacy compliance. These practical consideration shall be included in system design at the very beginning of development cycle.
Closing Thoughts
While Memory Management falls neatly within the broader Context Engineering, it is more impactful and complex than tool calling or MCP. Agentic Memory Management to enable continuous learning is the next frontier for a more personalized AI application that can anticipating user needs and wants before they even need to ask.
I had so much fun taking this course and look forward to continue learning/developing AI-powered application for personal productivity.
Here is the demo and Github repo for my project. Please feel free to leave a comment / suggestion. Thanks!
Top comments (0)