How Do You Implement an Enterprise Context Layer for AI?

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by Emily Winks, Data governance expert at Atlan.Last Updated on: February 12th, 2026 | 12 min read

Quick answer: How do you implement a context layer for AI?

Context layers transform raw data into actionable intelligence by providing AI systems with structured, task-relevant information in real time. Enterprise AI systems require reliable context about definitions, relationships, and operational rules to avoid hallucinations and make accurate decisions.

Key strategies for implementation:

  • Contextual storage architecture: Knowledge graphs, vector databases, semantic models.
  • Context engineering techniques: RAG patterns, prompt optimization, semantic filtering.
  • Lifecycle management: Versioning, testing frameworks, continuous refinement.
  • Session orchestration: State tracking, memory systems, temporal qualifiers.
  • Governance integration: Policy enforcement, audit trails, access controls.

Below, we'll explore: building contextual storage foundations, engineering context delivery systems, implementing lifecycle and session management, comprehensive system layers, how modern platforms streamline implementation.

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Building contextual storage foundations

Context implementation starts with architectural decisions about how organizational knowledge gets stored and retrieved. The foundation determines whether your AI agents access reliable information or operate in a vacuum.

Knowledge graph architecture


Knowledge graphs structure how entities connect across your business domains. For example, financial services implementations capture relationships between risk assessment processes, customer onboarding workflows, sanctions screening procedures, and investigation protocols—treating them as interconnected elements rather than isolated data silos.

Organizations like Mastercard unified 100M+ assets across thousands of metadata systems by implementing graph-based context storage. The architecture enabled fraud detection systems to trace relationships between transactions, merchants, and customer patterns in milliseconds rather than hours.

Vector database configuration


Vector databases enable semantic search across unstructured organizational knowledge. Technical documentation, policy manuals, conversation transcripts, and historical decisions transform into queryable embeddings that AI systems retrieve based on conceptual similarity rather than keyword matching.

Implementation requires selecting appropriate embedding models, establishing chunk sizes that balance context richness with retrieval precision, and creating indexing strategies that support sub-second query performance at enterprise scale.

Semantic layer integration


Semantic layers map technical database structures to business concepts. When analysts request “quarterly revenue by region,” semantic models automatically translate this into correct SQL joins across customer, transaction, and geography tables without requiring SQL expertise.

Modern implementations treat semantic definitions as inputs that feed into broader context layers. Metric calculations from semantic models combine with usage patterns, governance policies, and operational intelligence to provide AI systems with complete understanding of organizational concepts.

Context store components


Production context layers implement four integrated storage patterns:

  • Graph databases for relationship mapping and entity resolution
  • Vector stores for semantic search across unstructured knowledge
  • Rules engines for policy enforcement and exception handling
  • Temporal databases for version control and change tracking

Workday cataloged 6 million assets and established 1,000 glossary terms in a unified context store. The foundation enabled their data supply chain to feed both BI dashboards and AI systems with consistent business definitions.

Engineering context delivery systems

Storage foundations enable retrieval, but delivery systems determine whether AI receives relevant information at inference time. Context engineering optimizes how organizational knowledge flows into model reasoning.

RAG pattern implementation


Retrieval-Augmented Generation dynamically retrieves relevant information from external knowledge bases and incorporates it into AI model context windows. Instead of relying solely on training data, RAG systems pull current, domain-specific information exactly when needed.

Implementation moves beyond naive approaches that retrieve large unrefined chunks. Production RAG 2.0 systems employ semantic filtering and multi-hop retrieval—cascading smaller targeted queries through knowledge graphs before performing vector searches, then using specialized models to summarize retrieved information before passing to primary LLMs.

Context window optimization


Large context windows create “lost in the middle” problems where models struggle to find relevant information buried within millions of tokens. Recent research demonstrates that AI language models perform best when relevant information appears at the beginning or end of inputs, with performance degrading significantly for mid-sequence placement.

Context layers shift the burden of paying attention from models to architecture. Semantic filtering uses smaller tuned models to analyze full history and incoming data, identifying and discarding information semantically irrelevant to current steps. This ensures AI receives high-signal tokens that maximize likelihood of desired outcomes.

Progressive disclosure patterns


Letting agents navigate and retrieve data autonomously enables incremental context discovery through exploration. Each interaction yields information that informs the next decision—file sizes suggest complexity, naming conventions hint at purpose, timestamps proxy for relevance.

Agents assemble understanding layer by layer, maintaining only necessary information in working memory while leveraging note-taking strategies for additional persistence. This self-managed approach keeps agents focused on relevant subsets rather than drowning in exhaustive but potentially irrelevant information.

Prompt caching strategies


OpenAI documentation indicates prompt caching can save up to 90% of token costs while reducing latency. Structured context layers enable massive decision context—complete API specifications, brand guides, database schemas—to live in cached prefixes that load once and access repeatedly at minimal cost.

The architectural pattern separates operational context (high-velocity ephemeral task state) from decision context (stable low-velocity codified knowledge). Operational context like user IDs and current errors stays fresh, while decision context like refund policies and schemas leverages cache efficiency.

Implementing lifecycle and session management

Production AI systems require mechanisms for managing how context evolves over time and maintains consistency across interactions. Lifecycle and session management prevent context drift while enabling continuous improvement.

Version control systems


Context layers must treat governance as the control plane for AI operations. Organizations like CME Group established versioning for 1,300 glossary terms, ensuring analysts and AI agents accessed certified definitions while maintaining audit trails of how business logic evolved.

Implementation involves establishing workflows where context changes require approval before promotion to production, maintaining rollback capabilities when updates introduce unexpected behavior, and documenting rationale for modifications to create institutional memory about decision evolution.

Testing and validation frameworks


Organizations now leverage automated validation through systematic test generation from existing analytics dashboards and business intelligence reports. Modern implementations enable dashboard simulation generation to compress build timelines by automatically converting thousands of reports into test cases.

This approach enables robust QA functions before shipping rather than discovering issues in production. Teams establish confidence through systematic testing across representative query patterns, edge cases, and domain-specific scenarios.

Continuous feedback loops


Context remains trustworthy only when evolution becomes visible. Every AI interaction becomes an opportunity to refine business logic, resolve ambiguity, and increase shared understanding between human expertise and machine reasoning.

Implementation requires balancing accessibility with rigor through human-in-the-loop processes that capture corrections and improvements systematically. Organizations establish workflows where domain experts review AI outputs, flag inaccuracies, and contribute refinements that strengthen context for future interactions.

Context drift detection


Models fail when training data conflicts with current reality—deprecated APIs, changed policies, updated procedures. Context drift represents temporal mismatch that no amount of prompt engineering can fix.

Production systems implement change tracking to detect semantic shifts, monitor when model outputs diverge from expected patterns, and alert teams when context requires updates. Automated detection prevents silent degradation where AI continues generating plausible but incorrect responses based on outdated organizational knowledge.

Comprehensive system layers

Enterprise context layers operate as unified infrastructure serving both human analysts and autonomous agents. Architecture spans multiple integrated layers that work together to deliver reliable, governed context at inference speed.

Ingestion and extraction layer


DigiKey unified six critical systems cataloging 1M+ assets during their supply chain resilience initiative. The ingestion layer automatically extracted metadata from ERP transactions, IoT sensor feeds, shipment logs, and port event systems—establishing common business vocabulary with over 1,000 glossary terms.

Production ingestion implements automated context extraction from documents, conversations, and existing systems rather than manual documentation. Systems observe human activity through logs, tickets, chats, and screen recordings to infer behavioral rules, test those rules on real-world data, then iterate until achieving maximum accuracy.

Semantic translation layer


Context layers bridge natural language and structured queries through semantic translation. When business users ask “which high-value customer orders are at risk,” the system translates this into graph traversals that identify relevant entities, retrieve associated data, and construct responses grounded in organizational definitions.

Model Context Protocol servers enable AI agents to programmatically access graph context without manual integration work. This standardization allows LLMs to retrieve relationship context dynamically rather than requiring custom integration code for each AI application.

Governance and policy layer


General Motors tagged 98% of cloud datasets with automated classification before deployment by shifting governance left into development workflows. Metadata collection happens automatically through GitHub and YAML templates before release, with AI governance policies embedded directly into engineering processes.

Implementation ensures every feature containing data gets certified, discoverable, and ready for AI consumption through automated lineage, quality checks, and policy enforcement rather than retrofitted compliance.

Retrieval and activation layer


Context delivery to AI systems happens through standardized protocols rather than brittle point-to-point integrations. Production implementations expose context programmatically so AI assistants retrieve definitions, relationships, and operational rules in milliseconds at inference time.

The activation layer eliminates gaps between where context lives and where AI systems operate, bringing organizational knowledge directly into developer environments, conversational interfaces, and autonomous agent workflows.

How modern platforms streamline context layer implementation

Organizations succeeding with production AI treat context as infrastructure rather than application-specific configuration. Integrated platforms handle complexity so teams focus on applying AI to business problems rather than solving technical plumbing challenges.

Unified metadata architecture


Atlan’s Metadata Lakehouse provides Iceberg-native storage, real-time event streaming, and knowledge graphs for semantic understanding. This architecture creates unified context layers for structured, semi-structured, and unstructured data—making metadata analytics readily accessible and purpose-built for AI.

Organizations report faster time-to-value, broader adoption across teams, and more consistent AI outcomes compared to assembling separate infrastructure components. The platform handles orchestration complexity while teams concentrate on context enrichment and business logic refinement.

Automated enrichment capabilities


  • Column-level lineage tracking data transformations end-to-end
  • Intelligent automation enriching metadata continuously
  • Native AI assistants understanding organizational context
  • Extensible framework for building custom AI-powered workflows

Native governance integration


Implementation embeds policy enforcement, access controls, and audit trails directly into AI workflows rather than bolting compliance onto deployed systems. Organizations establish data products with embedded context, implement governance policies that scale, and maintain quality frameworks ensuring AI works with trusted data.

Context product frameworks


Workday pioneered context products as structured representations combining data with shared understanding. These verified, reusable units of organizational knowledge package facts, relationships, rules, and definitions into trusted artifacts that AI systems and humans both consume.

Implementation enables teams to create context products that evolve through feedback loops, drawing from company knowledge bases and organizational structures while continuously improving through systematic refinement.

Real stories from real customers: Context layers powering AI systems

Context as culture: Workday’s semantic foundation

“All of the work that we did to get to a shared language amongst people at Workday can be leveraged by AI via Atlan’s MCP server.” — Joe DosSantos, VP of Enterprise Data and Analytics, Workday

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Context by design: Mastercard’s trust architecture

“We have moved from privacy by design to data by design to now context by design. We chose Atlan, a platform that’s configurable, intuitive, and able to scale with our 100M+ data assets.” — Andrew Reiskind, Chief Data Officer, Mastercard

Watch the session →

Key takeaways

Context layer implementation determines whether AI systems deliver accurate insights or generate plausible hallucinations. Organizations succeeding with production AI establish unified storage architectures combining knowledge graphs with semantic models, engineer retrieval patterns that deliver relevant information at inference time, implement lifecycle management ensuring context evolves reliably, and activate context directly in workflows rather than maintaining separate systems.

The foundation requires treating context as shared infrastructure serving all AI applications rather than rebuilding organizational knowledge for each use case. Modern platforms accelerate deployment by handling technical complexity while teams focus on encoding business logic and refining definitions through systematic feedback.

Atlan’s unified context layer enables trusted AI deployment at enterprise scale.

Explore how Atlan’s context layer supports enterprise AI implementation
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FAQs about implementing enterprise context layers for AI

1. What’s the difference between context engineering and prompt engineering?


Prompt engineering focuses on writing effective instructions for individual tasks. Context engineering builds comprehensive information systems that work across multiple interactions and adapt to changing situations. Context engineering encompasses prompt design as one component within broader strategies for managing what information AI systems access.

2. How long does context layer implementation typically take?


Organizations using modern platforms with pre-built connectors typically achieve initial deployment in 60-90 days for priority domains. Building custom infrastructure from scratch can require 6-12 months. Implementation timelines depend on organizational complexity, existing metadata quality, and whether you’re starting from established semantic models or creating governance frameworks from scratch.

3. Can context layers work with multiple AI providers?


Yes. Properly implemented context layers use standardized protocols like Model Context Protocol that enable any AI system to retrieve organizational knowledge programmatically. This prevents vendor lock-in while ensuring consistency across different AI applications accessing the same context foundation.

4. How do you prevent context layers from becoming outdated?


Production systems implement continuous feedback loops where human corrections and AI interactions refine business logic systematically. Automated change tracking detects when operational reality diverges from context definitions. Version control enables teams to test updates before promotion while maintaining rollback capabilities if modifications introduce unexpected behavior.

5. What’s the relationship between RAG and context layers?


RAG is a retrieval technique; context layers provide the infrastructure containing what gets retrieved. Context layers ensure RAG implementations retrieve verified business meaning, relationships, and rules rather than just semantically similar documents. Research shows RAG grounded in well-structured, trusted context significantly reduces hallucinations compared to unstructured document retrieval.

6. Do context layers require specialized technical expertise?


Initial architecture benefits from understanding knowledge graphs, vector databases, and semantic modeling. However, modern platforms abstract much of this complexity through automated connectors and intelligent enrichment. Teams focus on business logic—defining terms, establishing relationships, capturing rules—while platforms handle technical implementation details.

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