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The New Stack Podcast

LLMs and AI Agents Evolving Like Programming Languages

Feb 20, 2025
Yam Marcovitz, tech lead at Parlant.io and CEO of emcie.co, dives into the evolution of large language models (LLMs) and their comparison to programming languages. He discusses how LLMs have progressed from simple text generation to more sophisticated reasoning and decision-making capabilities. Marcovitz highlights the importance of attentive reasoning queries for maintaining accuracy and consistency in AI interactions. He also addresses the subjectivity inherent in AI interpretation, emphasizing the need for nuanced approaches in developing AI agents, especially for customer service.
28:08

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

  • The evolution of large language models (LLMs) mirrors the progression of programming languages, with increasing complexity facilitating better decision-making and reasoning capabilities in AI agents.
  • ParLent’s open-source framework fosters innovation and precise AI interactions by allowing customizable guidelines that align with diverse stakeholder expectations in customer engagements.

Deep dives

The Evolution of AI and Decision-Making

The development of AI agents, particularly large language models (LLMs), is compared to the evolution of programming languages, highlighting how foundational technologies progress over time. Currently, LLMs are in an early stage of development, lacking the stability and tools necessary for widespread building upon them. The transition from basic transformer models to more complex systems is evident, with innovative frameworks emerging to facilitate decision-making in customer interactions. One such framework is ParLent, which emphasizes modularization to enhance how AI agents adhere to guidelines and manage complex interactions.

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