Building AI Agents and Enhancing Search with Conversational Follow-ups
Recent blog posts highlight two key trends in AI development: a focus on practical frameworks for building and iteratively improving AI agents, and the introduction of enhanced user-facing capabilities, such as conversational follow-ups and gaming tips in Circle to Search. Together, these updates emphasize starting with targeted, real-world applications, refining based on feedback, and delivering richer, more interactive AI-driven experiences for end users.
| Title | Source | Summary |
|---|---|---|
| How to Build an Agent | LangChain | The blog post discusses a practical framework for building agents, using an example of creating an email agent. Key steps include defining the agent’s job with examples, designing an operating procedure, building a minimum viable product (MVP) with focused reasoning tasks, connecting and orchestrating real data inputs, testing and iterating for accuracy and efficiency, and finally deploying, scaling, and refining the agent based on user feedback. The post emphasizes starting small, focusing on practical use cases, and iteratively improving the agent for real-world effectiveness. |
| Dive deeper with AI Mode and get gaming help in Circle to Search | Google AI | The blog post announces the introduction of new AI capabilities to Circle to Search. The update enables users to ask follow-up questions in AI Mode, allowing for a more in-depth exploration of topics. Additionally, users can now receive gaming tips through this feature. |