Understanding the Missing Piece: The Context Layer for AI Agents
In recent years, AI models have come a long way in their capabilities. Just two years ago, these models struggled with complex tasks; now, they are achieving scores in the top 1% of standardized te…
In recent years, AI models have come a long way in their capabilities. Just two years ago, these models struggled with complex tasks; now, they are achieving scores in the top 1% of standardized tests. However, when it comes to answering simple business questions, these agents often fall short, baffling those who deploy them in real-world scenarios. After initial enthusiasm, many businesses find themselves ditching these AI solutions within a month of deployment. What’s missing? Context. Prukalpa Sankar, Co-Founder and CEO of Atlan, addresses this exact issue by introducing the concept of the Context Layer. Drawing on insights from hundreds of AI deployments, she explains how context—comprising business definitions, procedural knowledge, and operational norms—elevates an AI agent from a simple demo tool to an indispensable business resource. Based on content fromAI EngineerWhy AI Agents Need ContextThe essence of Prukalpa’s argument is that cognitive intelligence alone doesn’t equate to effectiveness in the real world. Studies show that cognitive intelligence accounts for merely 10% of job performance variance. Instead, for AI—and human employees—contextual intelligence is crucial. AI models have seen exponential enhancement in their problem-solving capabilities, yet they lack the ‘situated knowledge’ that business contexts demand. Lessons from Human LearningTo convey the importance of context, Prukalpa shares a narrative around a fictional employee named Maya. Maya is a data analyst who has mastered her role at a fast-food company called McContext Burgers. Her skill in answering complex questions, such as why a metric has changed, comes not from innate intelligence alone but from her understanding of the business context. She considers variables that only a well-rounded expert would know, such as weather patterns or recent product launches. The Architecture of a Context LayerIn the technology realm, a Context Layer can be likened to “GitHub for context. ” It consists of versioned, testable, and portable repositories that hold and manage business knowledge. These repositories work with simulation environments to catch potential failures before systems are deployed. They also involve agent traces—logging mechanisms that contribute learned experiences back to shared repositories. Building an Efficient Company BrainAt Atlan, Prukalpa and her team have been developing an internal ‘company brain,’ a repository where all relevant skills and context are stored and accessible. They focus on compounding learning loops where each skill or agent learns and shares back its knowledge, benefiting the entire system. Prukalpa emphasizes that for efficient AI integration, context engineering should be prioritized. Unlike fine-tuning models or adjusting prompts, establishing a robust context layer ensures that agents are versatile and beneficial across all business activities. This open-context model allows deployment across various platforms, like MCP or Iceberg, circumventing the drawbacks of isolated agent systems where context lives in silos. Challenges and Future DirectionsOne of the significant challenges of developing context layers is their management, akin to code. Dependency management, quality assurance, and governance complicate the setup. Prukalpa calls for innovations such as ‘skills with profiles’ that can self-update and store valuable company knowledge, adhering to security measures and maintaining consistent quality. ConclusionThe frontier for AI-enhanced business lies not in merely advancing model intelligence but in nurturing the contextual framework around it. In an era where ‘Context is IP,’ companies should shift their focus towards transforming isolated, intelligent agents into a cohesive, context-rich network that serves their unique business environments. Prukalpa’s pioneering vision is a clarion call for enterprises aiming to maximize the impact of AI by investing in a foundational context layer, ensuring that AI systems are not just knowledgeable but profoundly effective in delivering real-world outcomes. Frank#DataScientist, #DataEngineer, Blogger, Vlogger, Podcaster at http://DataDriven. tv . Back @Microsoft to help customers leverage #AI Opinions mine. #武當派 fan. I blog to help you become a better data scientist/ML engineer Opinions are mine. All mine.