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Harness Engineering vs Prompt Engineering vs Context Engineering

Harness Engineering vs Prompt Engineering vs Context Engineering

Prompt engineering, context engineering, harness engineering: three disciplines, three failure modes. Learn where each layer breaks down and how Atlan's Context Layer ships with Context Agents that continuously validate definitions, ownership, and lineage across 75+ connectors , ensuring the data estate that prompt, context, and harness operations run against is governed and semantically consistent before any agent call executes.

Data Contracts for AI: Why They Matter More Than Ever

Data Contracts for AI: Why They Matter More Than Ever

Data contracts for AI need more than schema and quality rules. Learn why semantic enforcement at inference time matters and how Atlan closes the gap.

Data Lakehouse for AI: Why Storage Needs a Metadata Layer

Data Lakehouse for AI: Why Storage Needs a Metadata Layer

A data lakehouse for AI combines reliable storage with governed metadata. Atlan adds certified definitions, lineage, and policy so AI agents trust the data.

Data Mesh and AI: How Decentralized Architecture Affects Agents

Data Mesh and AI: How Decentralized Architecture Affects Agents

Data mesh and AI create a governance gap: AI agents cross domain boundaries a data mesh was never built to govern. Learn how a shared context layer closes it.

ETL vs. ELT for AI: Why Your Pipeline Needs a Third Stage

ETL vs. ELT for AI: Why Your Pipeline Needs a Third Stage

ETL vs. ELT for AI: ELT wins the storage argument for most workloads, but not the context one. See why AI pipelines need a governed context layer in 2026.

How to Build a Knowledge Base for AI Agents

How to Build a Knowledge Base for AI Agents

Building a knowledge base for AI agents takes five layers: ingestion, hybrid retrieval, reranking, evaluation, and a governed semantic layer. Here's how.

How to Connect Enterprise Data Sources to LLMs Securely

How to Connect Enterprise Data Sources to LLMs Securely

How to connect enterprise data sources to LLMs securely: enforce access control, mask PII, manage secrets, and meet residency rules at every connection.

How to Handle PII and Sensitive Data in AI Pipelines

How to Handle PII and Sensitive Data in AI Pipelines

Learn how to handle PII in AI pipelines: discovery, dynamic masking, lineage, and MCP-gated access that keep sensitive data safe before it reaches a model.

Real-Time Data for AI Agents: Streaming and Event Architecture

Real-Time Data for AI Agents: Streaming and Event Architecture

Real-time data for AI agents needs more than fast streaming; agents need context freshness, lineage, and policy state before they can trust an event and act.

Semantic Search Implementation: From Embeddings to Retrieval

Semantic Search Implementation: From Embeddings to Retrieval

Semantic search implementation converts documents to embeddings retrieved by meaning, from vector stores to the governed context layer AI agents need.

Structured vs. Unstructured Data for AI: Unified Context Guide

Structured vs. Unstructured Data for AI: Unified Context Guide

Structured vs. unstructured data for AI creates a context gap: agents need both, unified by shared meaning. See how a governed context layer closes it.

How To Version LLM Training Data for Reproducibility

How To Version LLM Training Data for Reproducibility

Learn how to version LLM training data for reproducibility. Compare DVC, lakeFS, and MLflow, meet EU AI Act rules, and govern datasets at scale.

Data Lineage for AI: Tracing AI Answers to Source

Data Lineage for AI: Tracing AI Answers to Source

Data lineage for AI traces each agent output to source context, definitions, and data. Learn what your team needs to make AI answers auditable.

Data Observability for AI Pipelines: A Practitioner Guide

Data Observability for AI Pipelines: A Practitioner Guide

Data observability for AI pipelines monitors quality, freshness, schema, and lineage. Here is the sixth pillar AI agents add: semantic integrity.

Feature Store for ML: How It Fits in the Modern AI Stack

Feature Store for ML: How It Fits in the Modern AI Stack

A feature store solves training-serving skew and enables feature reuse across ML teams. Here is how it works and what governance layer belongs above it.

How to Prepare Enterprise Data for AI Agents: Six-Step Guide

How to Prepare Enterprise Data for AI Agents: Six-Step Guide

A six-step sequence to make your data estate machine-readable for AI agents. Governance and evaluation gaps block most pilots. Here is the fix.

Metadata Management for AI: Why Metadata Is the New Data

Metadata Management for AI: Why Metadata Is the New Data

A governed context layer makes your definitions, lineage, and policies queryable by AI agents at inference time. Learn why documentation-only metadata fails AI.

Multimodal Data for AI Agents: Text, Images, and Video

Multimodal Data for AI Agents: Text, Images, and Video

Learn how enterprises handle text, images, and video for AI agents using metadata, lineage, multimodal governance, and shared context layers.

Synthetic Data for AI Training: Use Cases and Risks

Synthetic Data for AI Training: Use Cases and Risks

Synthetic data solves data scarcity, privacy constraints, and class imbalance in AI training. Here is where it works, where it fails, and how to govern it.

Systems of Record, Data, and Knowledge: Enterprise Stack

Systems of Record, Data, and Knowledge: Enterprise Stack

Systems of record, data, and knowledge are the three categories of enterprise truth. Here is what they mean for AI agents querying across all three.

Systems of Semantics: The Fourth Enterprise Data System

Systems of Semantics: The Fourth Enterprise Data System

Systems of semantics govern which meaning your AI agents use, for whom, and when. Here is why AI makes this the fourth enterprise data system type.

Data Catalog vs Context Layer: What AI Agents Actually Need

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.

Agent Skill Best Practices: What Makes a Skill Reliable in 2026

Agent Skill Best Practices: What Makes a Skill Reliable in 2026

Agent skill best practices cover progressive disclosure, scoping, and testing, but skip whether the data a skill reads from can be trusted at runtime.

Agent Skills vs MCP: What's the Difference and When to Use Each

Agent Skills vs MCP: What's the Difference and When to Use Each

Agent Skills teach an AI agent how to do a task; MCP gives it live access to tools and data. Compare Agent Skills vs MCP: architecture, auth, and failure modes.

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