According to @DeepLearningAI, Genspark CTO Kay Zhu told AI Dev 25 x NYC that Genspark prioritizes autonomous AI agents that plan over rigid workflows and equips them with 80+ specialized tools, arguing workflows fail on edge cases and compound errors while agents can observe, backtrack, and recover, which is directly relevant to AI infrastructure demand and agentic AI adoption themes that traders track across equities and crypto narratives. Source: DeepLearning.AI. According to @DeepLearningAI, the explicit emphasis on agent autonomy and tool breadth is a concrete product signal for the agentic AI stack, with the full talk linked for due diligence, making it a timely catalyst marker for investors monitoring developer momentum and enterprise adoption tied to the agentic AI narrative. Source: DeepLearning.AI. According to @DeepLearningAI, the session and shared video link provide verifiable details for trade research, including the count of 80+ tools and the operational rationale (edge-case recovery) that market participants can use to benchmark vendor capabilities in the agentic AI segment. Source: DeepLearning.AI.