Question 1

What is context engineering for AI agents?

Accepted Answer

Context engineering for AI agents is the practice of assembling the instructions, tools, and validated information an agent needs at each step of a task. It has two halves. The runtime half governs what enters the context window and in what order. The supply half governs where that information comes from, how it is defined, and who has approved it before an agent can retrieve it.

Question 2

Is RAG enough for enterprise AI agents?

Accepted Answer

Retrieval-augmented generation (RAG) solves reach, not correctness. It connects an agent to information outside its training data, but it does not establish which of several conflicting definitions is authoritative, whether a table is production-grade, or whether a column has been deprecated. In an enterprise, those questions cause more agent failures than retrieval quality does. RAG works well when it retrieves from a governed source and poorly when it retrieves from an ungoverned one.

Question 3

How do coding agents like Claude Code get enterprise data context?

Accepted Answer

Through a context layer that sits between the agent and the data platform, usually exposed over an MCP server. Miro uses this pattern: Claude Code orchestrates question decomposition, DataHub supplies governed metadata including entity descriptions, glossary terms, lineage, and example queries, and Snowflake Cortex executes the SQL. Without that layer, a coding agent connected directly to a warehouse sees schema and nothing else, which is why Miro's accuracy sat below 40% before the change.

Question 4

Who owns context engineering, AI engineers or data engineers?

Accepted Answer

Both, working on different halves. AI engineers own the runtime: prompts, tools, memory, and the agent loop. Data engineers own the supply: ingestion, semantic definitions, lineage, and governance. Most published guidance addresses the first group, which is why the second half is often unowned. DataHub covers the data engineering side in the data engineer's guide to context engineering.

Question 5

How do you measure whether context engineering is working?

Accepted Answer

Build an evaluation bank of real business questions with known-correct answers, and score against it continuously. Miro used 900 questions written by domain experts across finance, marketing, and product, run daily and refreshed with each model change. Aggregate accuracy is the headline number, but the more useful diagnostic is the failure breakdown: how many errors trace to routing, to sparse documentation, or to the model itself.

Question 6

Can context engineering be fully automated?

Accepted Answer

No. Automated derivation from query history, dbt projects, and BI definitions covers a large share of the work and should be used for all of it. But semantic meaning that was never written down anywhere cannot be inferred from a schema, which is why expert validation remains part of every program that works at scale. The realistic target is generation proposing and experts confirming, not experts authoring from scratch.

Question 7

What does a context platform do that a data catalog doesn't?

Accepted Answer

A data catalog is built for humans searching for data. A context platform is built to serve validated context to agents at machine speed. The practical differences are activation, validation, and freshness: an MCP server, skills, and APIs that agents consume directly, a DataHub Cloud workflow where domain experts approve machine-proposed definitions before agents see them, and event-driven updates that keep context current as the estate changes.

Question 8

What are the most common context engineering failure modes?

Accepted Answer

The runtime literature names four. Context poisoning, where an error enters the context and compounds as the agent builds on it. Context distraction, where accumulated history crowds out fresh reasoning. Context confusion, where irrelevant tools or documents pull the model toward the wrong action. And context clash, where contradictory material leaves the agent stuck between assumptions. All four describe what happens inside the context window during multi-step tasks. A fifth category sits upstream and is more common in enterprises: the retrieved context was accurate, well-formed, and wrong, because the definition behind it was never validated.

Question 9

Do larger context windows make AI agents more reliable?

Accepted Answer

Not reliably. A context window acts as the model's short-term memory during inference, holding instructions, retrieved documents, tool calls, and conversation history for the current task. It is finite, and expanding it does not necessarily improve performance. Research on long-context behaviour consistently finds that accuracy degrades as input grows, well before the advertised limit, and that models attend less reliably to material buried in the middle of a long context than to material at the start or the end. Efficient management matters more than raw size, because unnecessary token usage crowds out what the agent actually needs.

Context Engineering for AI Agents: Why the Hard Part Isn’t the Context Window

What Is a Business Context Layer? What It Holds and What It Changes

What Is Interoperable Context? Why Portability Is the Real Test

Context Management: The Foundation for Trustworthy AI Agents

Why Context Preparation Is Important (and Not Only for Agents)

What Is Data Interoperability? Why Connectors Aren’t Enough

Agents That Get it Right: How FIS Grounds Databricks Genie in Enterprise Context with DataHub

What Is Databricks Genie One, and Where Does It Fit in Your Stack?

What Is Snowflake Cortex Sense, and Where Does It Fit in Your Stack?

The Best Context Management Platforms for AI Agents in 2026

Why AI Agents Need Human-Validated Semantic Context

Context Layer Components for AI Agents: What It Takes to Build Each One

Blog Category: Context Management