Day 2 of diving deep into AI Agents!

Just finished the Google whitepaper, and it has a very detailed and solid breakdown of AI agents.

My takeaways:

Prompt Engineering Techniques: ReAct, CoT (Chain of Thought), and ToT (Tree of Thoughts) are ways to guide the agent's reasoning.

Tool Types: Extensions, functions, and data stores – the building blocks for agents to interact with the world.

Targeted Learning: In-context learning (one-shot/few-shot - quick but can be hit or miss), retrieval-based (more complex but flexible), and fine-tuning (more effort upfront for better results).

Out of the OpenAI guide, the Anthropic blog, and this Google paper, I'd say the whitepaper gave the most detailed overview so far.

Still eager to find a cool project to build to put this knowledge into practice!

If you're also exploring AI Agents, what resources have you found most helpful?

Links:

OpenAI's guide:

https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf

Google's whitepaper on AI Agents:

https://ia800601.us.archive.org/15/items/google-ai-agents-whitepaper/Newwhitepaper_Agents.pdf

Building effective agents

https://www.anthropic.com/engineering/building-effective-agents

Thanks for being a part of my learning journey.