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How To
How to Build an Enterprise Context Layer for AI [2026 Guide]
Build an enterprise context layer for AI in 4-8 weeks. Five steps: Enterprise Data Graph, Context Agents, Context Engineering Studio, and Context Lakehouse.
June 11, 2026Articles & tutorials
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Data Lineage
9 Best Data Lineage Tools in 2026 | A Complete Roundup of Key Capabilities
Compare the best data lineage tools for your data and AI stack, with key capabilities and commercial versus open-source options.
June 10, 2026
AI Agent Engineering
Agent Harness Failures: Anti-Patterns and Root Causes
88% of AI agent projects fail to reach production. Learn 13 named anti-patterns across architectural, execution, and data-layer tiers — and how to fix them.
June 10, 2026
AI Agents
How to Build an Agent Memory Layer on Your Data Catalog
Activate your data catalog as an AI agent memory layer via MCP. Six metadata types, three integration paths, and proven accuracy benchmarks are included.
June 10, 2026
Ai Agent
How to test context quality for AI agents
Test context quality for AI agents using golden datasets, A/B testing, freshness checks, and production trace reviews to ensure accurate grounding.
June 10, 2026
Ai Agent
Context Repository for AI Agents: A Practical Guide
Learn what a context repository is, what it contains, how it improves agent context, and why versioned, governed context matters for enterprise AI.
June 10, 2026
AI Agents
The AI Agent Cold-Start Problem Explained
AI agents cold-start when context is missing. Learn the two forms of the problem — session and organizational — and how enterprise memory layers address both.
June 10, 2026
Context layer
Context Drift Is the Silent Failure Mode for Your AI Model Layer
Context drift causes AI agents to reason over stale definitions with no error signal. Learn the three root patterns and how Atlan's context lineage detects drift before it reaches production.
June 10, 2026
Data & AI
Context Drift Detection: How to Catch the AI Blind Spot
Context drift detection finds when metadata feeding AI agents has gone stale, before models fail. Learn 3-layer taxonomy & how active metadata catches it.
June 10, 2026
Context Engineering
Context Engineering for Multi-Agent Systems: A Practical Architecture Guide
A practical architecture guide for shared context repositories, MCP, A2A, memory, governance, and decision traces in multi-agent AI systems.
June 10, 2026
Context Engineering
Context Engineering for RAG Agents
Learn how to upgrade RAG agents with governed context, graph lookup, compression, testing, and MCP-style delivery for enterprise AI systems.
June 10, 2026
AI Readiness
Context Graph vs Ontology: Key Differences for AI in 2026
Context graphs link data assets dynamically. Ontologies define the vocabulary that makes them interpretable. Learn how Atlan's Context Layer connects to 75+ data systems and exposes a live, queryable context graph , covering asset definitions, lineage paths, and access policies , that AI agents traverse at inference time via a native MCP server, delivering the operational context an ontology alone cannot provide.
June 10, 2026
AI & Data Governance
What Is Context Infrastructure for AI Agents?
Context infrastructure for AI agents spans protocol, delivery, and governed data substrate layers, determining what agents know, trust, and act on.
June 10, 2026
Context Layer
Context Layer for AI Agents: Enterprise Guide 2026
A context layer for AI agents gives every agent governed business meaning, identity, and lineage. See how it works and what it replaces in your stack.
June 10, 2026
Context Engineering and Agentic AI
Context Management Software: The 4 Categories Explained
Context management software splits into four categories: frameworks, RAG, agent platforms, and enterprise context. Here's which one your AI agents need.
June 10, 2026
AI Data Governance
What Is Contextual Intelligence in AI?
Contextual intelligence in AI depends on governed data, not just better models. Learn how RAG accuracy, source quality, and data governance determine AI outcomes.
June 10, 2026
AI Agents
In-Context vs. External Memory for AI Agents: Tradeoffs and When to Use Each
Compare in-context and external memory for AI agents — covering token costs, retrieval latency, accuracy, and when each architecture fits your use case.
June 10, 2026
Llm
Lost-in-the-Middle Problem: Why Position Matters in the Context Window
Learn why LLMs favor the start and end of a context window, how that breaks RAG and agents, and how governed context delivery improves accuracy.
June 10, 2026
Data Governance
LLM Knowledge Base Data Quality: Governed Data Is the RAG Problem
Poor data quality degrades LLM outputs. Learn the key standards, evaluation methods, and tools for building and maintaining accurate LLM knowledge bases at scale.
June 10, 2026
Data Governance
LLM Knowledge Base Freshness: Measure and Manage Staleness
Learn how to score and manage knowledge base freshness in RAG systems. Covers embedding lag, stale retrieval rate, coverage drift, and active metadata solutions.
June 10, 2026
AI and Data Governance
LLM Knowledge Base Staleness: How Active Metadata Fixes It
Understand what causes LLM knowledge base staleness, how to score content freshness, and automated strategies to keep your knowledge base accurate and current.
June 10, 2026
AI Agents
Types of AI Agent Memory: Semantic, Episodic, Procedural, In-Context
Learn the main types of AI agent memory — episodic, semantic, and procedural — and how each type shapes agent behavior, accuracy, and long-term reliability.
June 10, 2026
AI Infrastructure and Enterprise Context Graphs
Decision Traces: The Compounding Asset Your AI Is Missing
Learn how decision traces create compounding value for AI agents by capturing organizational reasoning, building institutional memory, and enabling autonomous decision-making at scale.
June 10, 2026
Data Governance
Who Owns the Context Layer?
The context layer is owned by the data platform team in centralized orgs or domain teams in federated architectures. Learn how to choose the right model.
June 10, 2026
