Kunal Kushwaha via YouTube Free video
A clear, practical explanation of how LLM agents work: treating the LLM as the agent's “brain,” tool calls as actions, and executed tools as the environment. The talk walks through the mechanics of function calling, prompts, schemas, and iterative tool-use loops. It covers strategies for reliable tool calling and the engineering needed for long tool-call chains, including caching, finite-state control, external memory, and techniques to prevent goal drift. Overall, it bridges classic RL concepts with modern LLM agent design to show how to build robust, long-horizon AI agents.
Register for the hackathon: https://www.wemakedevs.org/hackathons/assemblehack25
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Kunal Kushwaha is in Tier 3: good universities, respected companies, nonprofits and well-known teachers of our institution ranking (74/100).
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Harvard University · edX
Learn to use machine learning in Python in this introductory course on artificial intelligence.
