Agentic Horizons

This episode discusses AutoGen, an open-source framework designed for building applications using large language models (LLMs). Unlike single-agent systems, AutoGen employs multiple agents that communicate and cooperate to solve complex tasks, offering enhanced capabilities and flexibility. The episode highlights the following key aspects: • Conversable Agents: AutoGen's core strength lies in its...

This episode explores the challenges and evolving paradigms in AI application development, drawing from a research paper on project archetypes for AI development1. The episode examines how existing project management frameworks fall short in addressing the unique uncertainties of AI projects, leading to the emergence of a new archetype – the cognitive computing project. Traditional Archetypes vs....

This episode discusses a human-AI collaborative system called ArguMentor, which aims to provide readers with multiple perspectives on opinion pieces to help them develop more informed viewpoints. The system was created because opinion pieces often present only one side of a story, making readers vulnerable to confirmation bias, where they favor information that confirms their existing beliefs. Arg...

This episode examines a recent research paper that explores how Large Language Models (LLMs) can be used for planning in problem-solving scenarios, with a focus on balancing computational efficiency with the accuracy of the generated plans. • The traditional approach to planning involves searching through a problem's state space using algorithms like Breadth-First Search (BFS) or Depth-First Searc...

This episode explores the fascinating world of LLM-based agents and their growing impact on software engineering. Forget standalone LLMs, these intelligent agents are supercharged with abilities to interact with external tools and resources, making them powerful allies for developers. We'll break down the core components of these agents - planning, memory, perception, and action - and see how they...

This episode explores a groundbreaking framework called Reasoning via Planning (RAP). RAP transforms how large language models (LLMs) tackle complex reasoning tasks by shifting from intuitive, autoregressive reasoning to a more human-like planning process. • The episode examines how RAP integrates a world model, enabling LLMs to simulate future states and predict the consequences of their actions....