4 月 17-18 日，由 CSDN 与奇点智能研究院联合举办的「2026 奇点智能技术大会」将在上海·环球 港凯悦酒店隆重举行。 奇点智能技术大会由"全球机器学习技术大会"全新升级而来。翻阅过去两年的嘉宾演讲 PPT（ ⬇️⬇️可扫 码领取 ），一条贯穿产业的技术跃迁线格外清晰：从 2024 年行业热衷于基座模型打磨、深度预训练与 算力军备竞赛，到 2025 年全面沉淀于 RAG 落地、轻量化微调与 AI 编程工程化初探，再到如今 2026 年，全行业聚焦 Agent 工程攻坚与商业闭环打造。 奇点智能技术大会见证并引领着 AI 从实验室里的"高智商游戏"，进化为驱动企业提质增效的核心生产 力。 本次大会云集 BAT、英伟达、AWS、微软、小红书、vLLM、京东、昆仑万维、网易等国内外顶尖机构 与企业一线 AI 实践者 ，围绕 Agent 系统与工程、AI 原生应用创新与开发实践、AI Infra 基础设施与 运维等前沿议题展开分享。 | 4月17-18日 · 上海环球港凯悦酒店 | | | --- | --- | | 50+ 12大 | 1000+ | | 一线技术 专题覆盖 直击AI原生 | 产业精英 ...

Core Viewpoint - The emergence of "long-horizon agents" is reshaping the software engineering paradigm, moving from deterministic code-based systems to models that operate as black boxes, requiring real-time execution to understand their behavior [2][3][6]. Group 1: Long-Horizon Agents - Long-horizon agents are seen as a turning point in AI, with predictions that their adoption will accelerate by the end of 2025 to 2026 [2]. - These agents function more like "digital employees," capable of executing tasks over extended periods, learning from trial and error, and self-correcting [2][3]. - The transition to long-horizon agents may challenge traditional software companies, similar to the shift from on-premises to cloud solutions, where not all companies successfully adapted [2][3]. Group 2: Differences in Software Development - Traditional software development relies on deterministic logic written in code, while agent-based systems introduce non-deterministic behavior, making it necessary to observe their real-time execution to understand their operations [30][32]. - The concept of "tracing" has become crucial in agent systems, allowing developers to track internal processes and understand the context at each step, which differs significantly from traditional software debugging methods [31][32]. - The iterative process of developing agents is more complex, as developers cannot predict behavior before deployment, necessitating more rounds of refinement and adjustments [34][36]. Group 3: The Role of Data and Instructions - Existing software companies possess valuable data and APIs that can be leveraged in the agent era, but the ability to effectively utilize these assets will depend on new engineering approaches [37][38]. - The instructions on how to use data effectively are becoming increasingly important, as traditional methods of human execution are being automated through agents [38]. - The integration of domain-specific knowledge into agent systems is essential for their effectiveness, as seen in examples from the financial sector [38]. Group 4: Future of Agent Development - Memory capabilities in agents are anticipated to become a significant competitive advantage, allowing them to learn and improve over time [51][52]. - The development of user interfaces for long-horizon agents will likely require both synchronous and asynchronous management to handle tasks effectively [53][54]. - Code sandboxes are expected to become a critical component of agent capabilities, enabling safe execution and verification of scripts [56].