Agentic AI is the latest foray into big-bet promises for businesses and society at large. While promising autonomy and efficiency, AI agents raise fundamental questions about their accuracy, governance, and the potential pitfalls of over-reliance on automation. 

Does this story sound vaguely familiar? Hold that thought. This discussion about the over-under of certain promises is for you.

Show Notes

The economics of LLMs and DeepSeek R1 (00:00:03)

• Reviewing recent developments in AI technologies and their implications 
• Discussing the impact of DeepSeek’s R1 model on the AI landscape, NVIDIA 

The origins of agentic AI (00:07:12)

• Status quo of AI models to date: Is big tech backing away from promise of generative AI?
• Agentic AI designed to perceive, reason, act, and learn

Governance and agentic AI (00:13:12)

• Examining the tension between cost efficiency and performance risks [LangChain State of AI Agents Report]
• Highlighting governance concerns related to AI agents 

Issues with agentic AI implementation (00:21:01)

• Considering the limitations of AI agents and their adoption in the workplace 
• Analyzing real-world experiments with AI agent technologies, like Devin 

What's next for complex and agentic AI systems (00:29:27)

• Offering insights on the cautious integration of these systems in business practices
• Encouraging a thoughtful approach to leveraging AI capabilities for measurable outcomes

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