Summary

In this episode, Laura Fu and Rizurekh discuss the evolving landscape of AI agents, differentiating between true AI agents and deterministic workflows. They explore the nuances of agentic workflows, the challenges in building effective AI agents, and the importance of setting user expectations. The conversation also delves into technical considerations for AI agents and the distinction between agents and other AI tools like co-pilots. Real-world applications of AI agents are highlighted, showcasing their potential to enhance productivity and collaboration.

takeaways

• AI agents can perform tasks autonomously and reason through complex situations.
• Deterministic workflows often fail to account for all possible scenarios.
• Agentic workflows involve collaboration and decision-making with AI.
• Not all AI tools labeled as agents are truly autonomous.
• User expectations must be managed when deploying AI agents.
• Designing the user experience for AI agents is critical for success.
• Technical preparation and context management are essential for effective AI agents.
• AI agents should be able to communicate their capabilities to users.
• Search relevance and data preparation are key metrics for AI performance.
• Co-pilots can be agentic but are not necessarily agents.