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00:00 – Intro to AI agents
01:21 – Generic agent workflows explained
03:06 – Generic workflows for complex tasks
04:08 – Generic workflows improve GPT-3.5 & GPT-4
05:10 – Mixture of agents paper examples
08:13 – Open-source LLMs beat GPT-4 with mixture of agents
09:16 – Few real-world agent use cases currently
09:46 – No-code agent workflow demo in Cassidy AI
13:54 – Multi-On web browsing agent overview
16:27 – Rabbit AI device agent & early issues
19:22 – Google customer service agent demo
21:14 – Devin AI coding agent from Cognition Labs
22:20 – OpenAI agent to control user’s computer
23:32 – Meta’s engineering & monetization agents
24:59 – IMVU’s reasoning-based coding agents
26:26 – Challenges making reliable real-world agents
29:38 – Hard for agents to do multi-step tasks with few errors
32:45 – Google: 1-2 years to multi-modal AI assistants
34:26 – Bill Gates on very knowledgeable AI agents
36:18 – Nvidia CEO on future AI agent teams in business
39:07 – Risks of fully autonomous AI agents
41:01 – Key agent parts: tools, memory, planning, actions
42:27 – Better language models vs prompts to improve agents
43:48 – User interface design key for agent workflows
44:13 – New agent workflows for coding tasks
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