为什么95%的智能体都部署失败了？这个圆桌讨论出了一些常见陷阱

Core Insights - 95% of AI agents fail when deployed in production environments due to immature foundational frameworks, context engineering, security, and memory design rather than the intelligence of the models themselves [1][3] - Successful AI deployments share a common trait: human-AI collaboration design, where AI acts as an assistant rather than a decision-maker [3][21] Context Engineering - Context engineering is not merely about prompt optimization; it involves building a semantic layer, metadata filtering, feature selection, and context observability [3][12] - A well-structured Retrieval-Augmented Generation (RAG) system is often sufficient, yet many existing systems are poorly designed, leading to common failure modes such as excessive indexing or insufficient signal support [8][9] Memory Design - Memory should be viewed as a design decision involving user experience, privacy, and system impact rather than just a feature [22][23] - Effective memory design includes user preferences, team-level queries, and organizational knowledge, ensuring that AI can provide personalized yet secure interactions [27][29] Trust and Governance - Trust issues are critical for AI systems, especially in sensitive areas like finance and healthcare; successful systems incorporate human oversight and governance frameworks [18][21] - Access control and context-specific responses are essential to prevent information leaks and ensure compliance [20][21] Multi-Model Inference and Orchestration - The emerging design pattern of model orchestration allows for efficient routing of tasks to appropriate models based on complexity and requirements, enhancing performance and cost-effectiveness [32][34] - Teams are increasingly using a decision-directed acyclic graph (DAG) approach to manage model interactions, ensuring that the system can adapt and optimize over time [34] User Experience and Interaction - Not all tasks require conversational interfaces; graphical user interfaces may be more efficient for certain applications [39][40] - The ideal use of natural language processing occurs when it lowers the learning curve for complex tools, such as business intelligence dashboards [40][41] Future Directions - Key areas for development include context observability, portable memory systems, domain-specific languages (DSL), and delay-aware user experiences [43][44][46] - The next competitive barriers in generative AI will stem from advancements in memory components, orchestration layers, and context observability tools [49][52]