Agents in production answer business questions at machine speed, with no analyst between the query and the decision. When an agent reads raw schema, it infers what rev_adj_final_v2 means, and each application infers it a little differently.

Three gaps show up between the pilot and production:

• Grounding. Business logic lives in schema hints and system prompts, one application at a time, and breaks on the next schema change.
• Evidence. When an answer is questioned, you can't show which definition the agent used or where that definition came from.
• Cost. Agents that assemble context at runtime pay twice: compute to traverse the metadata graph, then tokens to generate SQL that may need retries.

What changes with MetaKarta

Compiled context, deterministic SQL. No compute tax, no token tax. MetaKarta MCP's Semantic Hub Tools return human-approved, compiled definitions and deterministic SQL. The agent's answer matches BI because it's the same definition.

Test the claim yourself, on your data, with the grounding visible. Context Sandbox runs test questions against live data, evaluates the answers, validates bindings, and shows the grounding before an agent ever sees it.

Every response returns the metadata evidence that produced it. MetaKarta MCP's Metadata Management Tools serve governed lineage, catalog, and governance metadata, with per-user access tokens so an agent sees exactly what the person behind it is authorized to see.

Changes caught before they reach an agent. Parser-based, column-level lineage shows which governed definitions depend on a column before a change deploys. Ask MetaKarta answers "What breaks if I rename this column?" through your own LLM provider.

How it works

MetaKarta runs on one shared metadata repository, so the agent, the dashboard, and the audit trail read the same definitions.

• MetaKarta MCP: One server, two toolsets: Metadata Management Tools for governed metadata, and Semantic Hub Tools for compiled context and deterministic SQL.
• Context Sandbox: Test questions, answer evaluation, binding validation, and grounding inspection on live data before deployment.
• Ask MetaKarta: Natural-language questions over the metadata estate through Anthropic, Google, Ollama, OpenAI, or Azure OpenAI, permission-aware.
• Ontology Modeling: Entities, relationships, and hierarchies built from parsed lineage. Every concept is bound to a real asset from day one.
• Structured AI consumption: Governed definitions compiled into Snowflake Semantic Views and Databricks Metric Views, which Snowflake Cortex Analyst and Databricks Genie read natively.
• Semantic Hub: The governed definition behind every answer, compiled once and served to BI tools and agents alike.

See it on your own estate

Three checks make a useful evaluation of any metadata platform, including ours:

1. The BI-match check. Pick one question your finance dashboard already answers. Ask the vendor's agent the same question and compare both the SQL and the number.
2. The evidence check. Call the vendor's MCP server and look at what comes back: descriptions, or a compiled definition with the metadata evidence that produced it, filtered by the caller's permissions.
3. The change check. Rename a column in a test schema and ask which definitions, and so which agent answers, it affects before anything deploys.

Built on nearly 30 years of metadata engineering

MetaKarta's metadata technology runs inside the products of Microsoft Purview, Informatica from Salesforce, IBM, Oracle, and Qlik Talend. MetaKarta brings that engine to your AI platform directly, with definitions that stay portable as native artifacts in the tools you already own.

When agents read compiled definitions, the number to watch is the share of agent answers that match BI on the questions your business already reports.

Get in touch to learn more about how MetaKarta MCP grounds agents in compiled context.