As developers, we know the pain: LLMs are powerful, but unreliable when disconnected from real-time data.
The solution isn't just better prompting; it's proper ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ using Gemini 3.0's inherent capabilities.
I just published a detailed guide on creating a Google Search-grounded agent, focusing on the API changes that make it truly robust.
๐๐ฒ๐ ๐ง๐ฒ๐ฐ๐ต๐ป๐ถ๐ฐ๐ฎ๐น ๐ง๐ฎ๐ธ๐ฒ๐ฎ๐๐ฎ๐๐ (๐ช๐ต๐ฎ๐ ๐๐ผ๐'๐น๐น ๐น๐ฒ๐ฎ๐ฟ๐ป):
โข ๐ง๐ต๐ฒ ๐ฃ๐ผ๐๐ฒ๐ฟ ๐ผ๐ณ ๐ง๐ต๐ผ๐๐ด๐ต๐ ๐ฆ๐ถ๐ด๐ป๐ฎ๐๐๐ฟ๐ฒ๐: Learn how to capture and feed the model's thoughtSignature back into the conversation history. This replaces complex "Chain of Thought" prompting and ensures reliable multi-turn actions.
โข ๐ง๐ต๐ฒ ๐ฆ๐๐/๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ ๐ฆ๐๐ฎ๐ฐ๐ธ: We use the Google GenAI SDK (or LangChain/LlamaIndex) to define the Google Search tool and orchestrate its use.
โข ๐ฃ๐ฟ๐ผ๐บ๐ฝ๐ ๐ฆ๐ถ๐บ๐ฝ๐น๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป: With thinking_level (low/high), you can ditch bloated, prescriptive system prompts and rely on the model's native reasoning engine.
๐ ๐ถ๐ฐ๐ฟ๐ผ-๐๐ ๐ฎ๐บ๐ฝ๐น๐ฒ: A single Gemini call can now decide if it needs to search the web, execute the search tool, and then use the results to answer, all while preserving context. This is the ๐๐๐ฎ๐๐ฒ๐ณ๐๐น, ๐ฟ๐ฒ๐น๐ถ๐ฎ๐ฏ๐น๐ฒ ๐ฎ๐ฏ๐๐๐ฟ๐ฎ๐ฐ๐๐ถ๐ผ๐ป we've been waiting for.
If you are looking to build a production-ready agent, grab the code and deep dive into the guide.
๐๐ง๐: Have you experimented with Gemini 3's thinking_level yet? Share your findings!
#AI #Gemini3 #AgenticAI #LangChain #MachineLearning #Developer
๐ฅ๐ฒ๐ฎ๐ฑ ๐๐ต๐ฒ ๐ณ๐๐น๐น ๐ด๐๐ถ๐ฑ๐ฒ ๐ฎ๐ป๐ฑ ๐๐ฒ๐ฒ ๐๐ต๐ฒ ๐๐ผ๐ฟ๐ธ๐ถ๐ป๐ด ๐ฐ๐ผ๐ฑ๐ฒ:
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