What Is Context, Really? How AI Gets It Wrong in 2026 скачать в хорошем качестве

What Is Context, Really? How AI Gets It Wrong in 2026

Context for AI agents is not a single file or database, it is a layered architecture spanning structured SOPs, unstructured Slack threads, tribal knowledge, and organizational memory that must be synthesized into the smallest possible signal for a model to reason accurately. The real debate in enterprise AI isn't whether to give models more context, but how to give them the right context. How context composition solves the AI accuracy problem: Context exists at multiple levels inside an organization. At the surface, there are documented procedures and SOPs — what one panelist calls "MCP federated search: just look it up and get it." But the more consequential context lives in the connective tissue: the Slack thread where a pricing decision was made, the account history that followed, the tribal knowledge that explains why a rule exists, not just what it is. Effective context composition means reconstructing that web of decisions across structured and unstructured sources into a synthesized knowledge graph and not blasting a 10-million-token window through every document in the organization. As one panelist puts it: "Did I give you the whole Bible, or did I give you the cliff notes?" Getting context right is an optimization problem, routing the smallest, most relevant signal to the model so it can reason well, rather than overwhelming it with raw data volume. This requires a federated approach: a common core of shared context anchored to the systems of record, with domain-specific context federated to the edges where the expertise actually lives. Context management is ultimately a team sport and not a technology problem. People, AI, and systems must come together with intentional change management; no model alone can reconstruct the institutional knowledge embedded in human minds and organizational history. Key Takeaways Context is not monolithic, it exists at fundamentally different levels of an organization. Simple, documented workflows are easy to retrieve via federated search. But the context that drives better AI outcomes is the harder kind: decisions buried in Slack threads, historical account data, and the tribal knowledge that explains the reasoning behind rules, not just the rules themselves. Bigger token windows are not the answer to the context problem. Feeding a 10-million-token window through every organizational document is an expensive, inefficient substitute for synthesis. The real goal is the "cliff notes", the smallest, highest-signal representation of organizational context that lets a model reason correctly without burning tokens on irrelevant noise. Tribal knowledge has shifted from liability to strategic differentiator. Eighteen months ago, tribal knowledge was something organizations wanted to extract and eliminate. Today, it is recognized as a competitive moat; and context graphs that can observe, synthesize, and activate that knowledge at scale are becoming core AI infrastructure. Context management must be federated, not centralized. Like data governance before it, context management works best when a common core is shared across the organization, while domain-specific context is owned and maintained at the edges where the expertise actually lives. This makes it a people-plus-AI-plus-systems problem, not a purely technical one. About Atlan Atlan is the leading active metadata platform that serves as the context layer AI agents and analysts use to generate accurate, role-appropriate answers across the enterprise. Atlan continuously captures organizational context — from documented business logic and metric definitions to the tribal knowledge embedded in Slack threads, lineage graphs, and historical decisions — synthesizing it into an activated metadata layer that AI systems can query in real time. Trusted by data teams at Nasdaq, Plaid, Univision, and WeWork, Atlan delivers end-to-end column-level lineage, federated governance, and AI governance capabilities that ensure AI-generated answers reflect how your organization actually thinks and operates. Recognized as a Leader in the Gartner Magic Quadrant for Metadata Management Solutions 2025 and named a Leader in the Gartner Magic Quadrant for Data & Analytics Governance 2026, Atlan helps organizations move from fragmented, token-heavy context strategies to production-ready AI analytics grounded in institutional knowledge. #ContextManagement #AIAgents #TribalKnowledge #DataGovernance #Atlan