Active Metadata
Active Metadata Management: Complete 2026 Guide
Active metadata keeps AI agents working on current, accurate context. Learn how it works, why static metadata fails, and how to activate it.
July 2, 2026Articles & tutorials
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Ai Agent
ADLC vs SDLC: How the Agent Development Lifecycle Differs
ADLC vs SDLC: ADLC manages probabilistic agent behavior; SDLC manages code. Covers 8 structural differences and the context lifecycle gap most teams miss.
July 2, 2026
Ai Agent
Agent Context Layer Tools Compared: 2026
Honest comparison of 5 agent context layer tools on governance depth, MCP support, and cross-platform portability. Find the right tool for your stack.
July 2, 2026
Ai Agent
Agent Context Layer vs Knowledge Base: When to Use Each
A knowledge base is the right choice for many AI teams. This guide explains exactly where the thresholds are — and when a context layer becomes necessary.
July 2, 2026
Ai Agent
Agent Harness vs Agent Framework: The Third Layer Both Miss
Understand the real difference between agent harnesses and frameworks, how they work together in production, and why both miss the governed context layer.
July 2, 2026
Ai Agent
What Is Agent Sprawl and Context Sprawl? Causes, Risks, and Fixes
Agent sprawl is uncontrolled AI agent proliferation across an enterprise. Learn its two dimensions, identity sprawl and context sprawl, and the structural fix.
July 2, 2026
Ai Agent
What Are AI Agent Primitives?
Most frameworks define 4 AI agent primitives. Enterprise deployments need 6: add a shared context layer and control plane to reach production.
July 2, 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.
July 2, 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.
July 2, 2026
Ai Agent
Why AI Agents Need Versioned Context
Context versioning for AI agents: why stale definitions break production AI and how to implement manifest IDs, promotion pipelines, and rollback.
July 2, 2026
Ai Agent
Data Sovereignty for AI Agents: Key Controls and Architecture
Data sovereignty for AI agents is enforced at the context layer, not the model. Learn how GDPR, SCHREMS II, and EU AI Act apply to agentic pipelines.
July 2, 2026
Ai Agent
Enterprise AI Agent Guardrails: A Practical Checklist for 2026
A practical enterprise AI agent guardrails checklist for 2026. Covers data access, context governance, EU AI Act compliance, and incident response.
July 2, 2026
Ai Agent
How to Fix AI Agent Memory Loss: Session Amnesia vs Context
AI agents fail multi-turn tasks at 35% because of session amnesia and organizational ignorance. This guide gives the 5-step diagnostic-and-fix sequence.
July 2, 2026![How to Choose an Agentic Framework for Enterprise [2026]](https://atlan.com/og/know-ai-agent-how-to-choose-agentic-framework-enterprise.png)
Ai Agent
How to Choose an Agentic Framework for Enterprise [2026]
Compare five agentic frameworks on 7 enterprise dimensions: weighted scorecard, org-profile routing, and the context architecture decision most guides skip.
July 2, 2026
Ai Agent
Knowledge Graph Tools Compared: Features, Pricing, and Use Cases
Compare top knowledge graph tools for enterprise AI: Neo4j, Stardog, Amazon Neptune, Ontotext GraphDB, and Atlan's Enterprise Data Graph. Find the right fit.
July 2, 2026
Ai Agent
Knowledge Graph vs Graph Database: Key Differences Explained
What separates a knowledge graph from a graph database? Learn how graph DBs store data and how knowledge graphs add semantic meaning for enterprise AI agents.
July 2, 2026
Ai Agent
Ontology vs Knowledge Graph: Difference and When to Use Each
Ontology vs knowledge graph: learn how ontologies define domain concepts and how knowledge graphs instantiate them as real entities for enterprise AI.
July 2, 2026
Ai Agent
Semantic Layer vs Data Catalog: How They Differ and Work Together
What is the difference between a semantic layer and a data catalog? Learn how dbt, Cube, and Atlan solve the enterprise AI data context problem.
July 2, 2026
AI Governance
What Is an AI Control Plane?
Learn what an AI control plane is, how it differs from MLOps and the data plane, its core components, and why enterprises need one for AI governance in 2026.
July 2, 2026
Context Engineering
Context Agents for Enterprises: A 2026 Guide
Context agents are AI teammates that help you become AI-ready. Learn how they work, where they fail, and how Atlan makes them production-ready.
July 2, 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.
July 2, 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.
July 2, 2026
Data Catalog
Data Catalog Examples: Real Use Cases & Implementation Guide
Real data catalog examples show where an AI agent finds the right table to answer a question, from financial compliance to AI agent use cases.
July 2, 2026
Data Governance
Data Catalog vs Context Layer: What AI Agents Actually Need
A data catalog organizes metadata for humans; a context layer delivers governed context to AI agents at runtime. Learn which each serves and when you need both.
July 2, 2026
