Core Insights - The article asserts that AGI represents the ability to "figure things out," marking a shift from the era of "Talkers" to "Doers" in AI by 2026, driven by Long Horizon Agents [2] - Long Horizon Agents are characterized by their ability to autonomously plan, operate over extended periods, and exhibit expert-level features across complex tasks, expanding from coding to various domains [3][4] - The emergence of these agents is seen as a significant turning point, with the potential to revolutionize how complex tasks are approached and executed [3][21] Long Horizon Agents' Explosion - Long Horizon Agents are finally beginning to work effectively, with the core idea being to allow LLMs to operate in a loop and make autonomous decisions [4] - The ideal interaction with agents combines asynchronous management and synchronous collaboration, enhancing their utility in various applications [3][4] - The coding domain has seen the most rapid adoption of these agents, with examples like AutoGPT demonstrating their capabilities in executing complex multi-step tasks [4][5] Transition from General Framework to Harness Architecture - The distinction between models, frameworks, and harnesses is crucial, with harnesses being more opinionated and designed for specific tasks, while frameworks are more abstract [8][9] - The evolution of harness engineering is particularly advanced in coding companies, which have successfully integrated these concepts into their products [12][14] - The integration of file system permissions into agents is essential for effective context management and task execution [24] Future Interactions and Production Forms - Memory is identified as a critical component for self-improvement in agents, allowing them to retain and utilize past interactions to enhance performance [35] - The future of agent interaction is expected to blend asynchronous and synchronous modes, facilitating better user engagement and task management [36] - The necessity for agents to access file systems is emphasized, as it significantly enhances their operational capabilities [39]

People use traces from the start to just tell what's going on under the hood. And it's way more impactful in agents than in single LLM applications because in single LM applications, you get some bad response from the LLM. You know exactly what your prompt is. You know exactly what the context that goes in is because that's determined by code and then you get something out. In agents, they're running and and and repeating and so you don't actually know what the context at step 14 will be because there's 13 ...