Snowflake Intelligence

Snowflake Intelligence is the supported Snowflake-native agent integration path for Metatate.

Metatate keeps its canonical governance tools and MCP server intact:

core.metatate_mcp
core.discover_context(input VARIANT) and peers

But Snowflake Intelligence custom tools bind named scalar parameters, not a single VARIANT request object. To bridge that mismatch, Metatate provides an adapter layer of Snowflake Intelligence-friendly wrappers:

core.agent_discover_context
core.agent_get_decision_context
core.agent_inspect_data_meaning
core.agent_inspect_governance_rules
core.agent_authorize_use
core.agent_validate_query_context
core.agent_explain_why

These wrappers forward into the canonical Metatate tools and return the same JSON payloads.

For authorization flows, core.agent_authorize_use returns the same matched_instructions payload as core.authorize_use, and core.agent_explain_why returns that same instruction payload plus the stored trace and snapshot metadata.

Prerequisites

Metatate installed and running in your account
At least one policy deployed so governance tables contain data
Snowflake Intelligence enabled for your account and role
An Intelligence agent created in a schema where your role can manage agents

Step 1: Create a Snowflake Intelligence agent

Open AI & ML -> Agents in Snowsight
Click Create agent
Choose a writable database and schema
Create an agent such as:
- object name: METATATE_TEST_AGENT
- display name: Metatate Test Agent

Step 2: Add the Metatate custom tools

Open the agent's Tools tab and add the following custom tools.

1. `core.agent_discover_context`

Resource type: function
Object: core.agent_discover_context
Parameters:
- database_name -- string
- schema_name -- string
- min_sensitivity -- string
- compliance_framework -- string
- has_pii -- boolean
- domain -- string

Use this for questions like:

Which governed tables contain PII in <database>?

2. `core.agent_get_decision_context`

Resource type: function
Object: core.agent_get_decision_context
Parameters:
- table_name -- string, required

Use this for questions like:

What decision context applies to <fully-qualified-governed-table>?

3. `core.agent_inspect_data_meaning`

Resource type: function
Object: core.agent_inspect_data_meaning
Parameters:
- table_name -- string, required
- column_name -- string

Use this for questions like:

What does <column_name> mean from a governance perspective for <fully-qualified-governed-table>?

4. `core.agent_inspect_governance_rules`

Resource type: function
Object: core.agent_inspect_governance_rules
Parameters:
- table_name -- string, required
- column_name -- string

Use this for questions like:

Which usage, validation, and transfer rules apply to <column_name> in <fully-qualified-governed-table>?

5. `core.agent_authorize_use`

Resource type: procedure
Object: core.agent_authorize_use
Parameters:
- table_name -- string, required
- operation -- string, required
- intended_use -- string, required
- actor_role -- string
- columns_csv -- string
- destination_system -- string
- destination_jurisdiction -- string
- consumer_jurisdiction -- string
- context_json -- string JSON object

Use this for questions like:

Can <role> export <fully-qualified-governed-table> to <destination> for <intended-use>?

Use this wrapper for the full authorization contract, including column-scoped requests and audit metadata.

6. `core.agent_validate_query_context`

Resource type: procedure
Object: core.agent_validate_query_context
Parameters:
- sql_text -- string, required
- operation -- string
- intended_use -- string
- actor_role -- string
- destination_system -- string
- destination_jurisdiction -- string
- consumer_jurisdiction -- string

Use this for questions like:

Validate this query for <intended-use>: SELECT <columns> FROM <fully-qualified-governed-table>

7. `core.agent_explain_why`

Resource type: procedure
Object: core.agent_explain_why
Parameters:
- decision_id -- string, required

Use this for questions like:

Why was decision <decision_id> denied?

Step 3: Save the agent

Save the agent after adding or editing the custom tools. Snowflake Intelligence chat threads do not reliably pick up unsaved tool changes.

Step 4: Test the first workflow

Start a new thread with the agent and ask:

What decision context applies to <fully-qualified-governed-table>?

Expected behavior:

the agent calls core.agent_get_decision_context
Metatate returns the standard decision-context JSON payload
the agent summarizes policy, sensitivity, instructions, compliance, and risk metadata

Practical prompt examples

Intent	Prompt	Expected wrapper
Discovery	`Which governed tables contain PII in <database>?`	`core.agent_discover_context`
Decision context	`What decision context applies to <fully-qualified-governed-table>?`	`core.agent_get_decision_context`
Meaning inspection	`Show me the governance meaning of <column_name> in <fully-qualified-governed-table>.`	`core.agent_inspect_data_meaning`
Rule inspection	`Which usage, validation, and transfer rules apply to <column_name> in <fully-qualified-governed-table>?`	`core.agent_inspect_governance_rules`
Authorization	`Can <role> export <fully-qualified-governed-table> to <destination> for <intended-use>?`	`core.agent_authorize_use`
Query validation	`Validate this query for <intended-use>: SELECT <columns> FROM <fully-qualified-governed-table>`	`core.agent_validate_query_context`
Decision explanation	`Why was decision <decision_id> denied?`	`core.agent_explain_why`

Troubleshooting

The agent says it hit a SQL compilation error

Most likely cause: you wired a canonical core.*(input VARIANT) tool directly into the agent instead of the corresponding core.agent_* wrapper.

The agent ignores a newly added tool

Save the agent, then start a new thread. Unsaved changes and old threads may not use the latest tool configuration.