LangChain Agent 年度报告：输出质量仍是 Agent 最大障碍，客服、研究是最快落地场景

Core Insights - The main obstacle for the practical application of AI Agents in 2025 is not cost but quality, specifically ensuring reliable and accurate content output [1] - By 2026, discussions among enterprises have shifted from whether to implement Agents to how to scale their use effectively and reliably [2] Group 1: Adoption and Implementation - Over half (57.3%) of surveyed industry professionals have already deployed Agents in production, with 30.4% actively developing them with clear launch plans [4][5] - The adoption rate is higher in larger enterprises, with 67% of companies with over 10,000 employees having implemented Agents, compared to 50% in companies with fewer than 100 employees [6] - The most common applications for Agents are in customer service (26.5%) and research/data analysis (24.4%), together accounting for over half of all use cases [10][15] Group 2: Quality and Challenges - Quality remains the primary barrier to widespread Agent adoption, with one-third of respondents identifying it as a major bottleneck, focusing on accuracy, relevance, and consistency of outputs [14][18] - Delay (20%) is the second-largest challenge, particularly in real-time applications like customer service, where response speed is critical [17] - For enterprises with over 2,000 employees, quality issues are the top concern, while security (24.9%) is the second most significant challenge [18] Group 3: Observability and Evaluation - Observability of Agent execution processes has become an industry standard, with 89% of enterprises implementing some form of observability, and 62% having detailed tracking capabilities [21][23] - Over half (52.4%) of companies conduct offline evaluations using test sets, while online evaluations are increasing, now at 44.8% [25][28] - A mixed evaluation approach is common, with nearly a quarter of teams using both offline and online methods, and reliance on human review remains high [33] Group 4: Model Usage and Trends - OpenAI's GPT models dominate usage, but over three-quarters of teams employ multiple models based on task complexity, cost, and latency [36] - More than one-third of organizations are investing in deploying open-source models for cost optimization and compliance reasons [38] - Programming Agents are the most frequently used in daily workflows, followed by research Agents, indicating a strong preference for tools that enhance coding and information synthesis [40][41]