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Best Data Catalogs mark: an unfinished lineage pathBest Data Catalogs

Readiness course

Readiness course: getting enterprise data ready for AI agents

Short answer

Ten lessons in three tracks for the people who own the data platform: what agent-ready means, how to build the context, and how to buy and roll out a platform. Each lesson takes five minutes or less and links to the reviews and tools on this site.

By The Lineage Desk · Best Data Catalogs · Published

Desk research from public vendor pages. Editorial assessment.

Track 1: Foundations

  1. Lesson 1 · 3 min read

    What agent-ready data actually means

    Agent-ready data is data an AI agent can find, understand, trust and use within access rules, without a person writing the context by hand for every question.

  2. Lesson 2 · 3 min read

    Catalog, semantic layer or AI context layer: which does what

    Catalogs inventory data for people, semantic layers define metrics once, and AI context layers assemble lineage, definitions and rules for agents at query time. Many products now span more than one.

  3. Lesson 3 · 3 min read

    How MCP delivers context to agents

    MCP gives agents a standard way to ask a catalog, context platform or semantic layer for context. What matters is what the server returns, how it is scoped and how fresh it is.

Track 2: Building the context

  1. Lesson 4 · 3 min read

    Lineage from the warehouse to the agent

    Agents need lineage that runs past the warehouse into transformation and BI, ideally at column level, because much business logic lives in dashboards and models.

  2. Lesson 5 · 2 min read

    Governance rules agents can read

    Governance helps agents only when certification, sensitivity and access rules travel with the context in a form the agent receives, and stay current as the stack changes.

  3. Lesson 6 · 2 min read

    Business definitions without waiting for a perfect semantic layer

    Agents need the same definitions people use. You can model them in a semantic layer, extract them from where they already live, or combine both.

  4. Lesson 7 · 2 min read

    Testing agent answers before you trust them

    A readiness project is not finished when the agent connects. You need a repeatable way to check answers, find missing context and catch drift.

Track 3: Buying and rollout

  1. Lesson 8 · 2 min read

    Running a proof of concept for an AI context platform

    A useful proof of concept connects your real stack, runs your own agent questions, and tests lineage, governance and freshness, in a few weeks rather than a few months.

  2. Lesson 9 · 2 min read

    Reading the pricing models for catalogs and context platforms

    Prices in this category are mostly unpublished, and the units differ: data assets, developers, users plus platform fees, semantic objects or marketplace contracts. Know which unit grows with your plans.

  3. Lesson 10 · 2 min read

    Making the case for an agent-readiness budget

    The data platform leader is best placed to make agents work on enterprise data. A strong case ties the budget to specific tasks, measured before and after, rather than to broad AI promises.