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Context sources allow agentic workflows to automatically query your data during investigations. When configured, agents can fetch transaction data, customer information, and support history without manual intervention.

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Overview

Context sources bridge your Connectors with agentic engines. While connectors establish the connection to your data, context sources configure how agents interact with that data during workflow execution.

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Supported Context Source Types

TypeConnectorAgent Capabilities
SQLSnowflake, Roe TablesNatural language queries, schema discovery, data filtering
APIZendeskTicket search, conversation retrieval, entity lookup

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Configuring Context Sources

Context sources are configured when setting up an agentic workflow (like AML Investigation). Each context source requires:

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Common Fields

FieldRequiredDescription
connection_typeYesType of connector (snowflake, zendesk, Roe Tables)
nameYesFriendly name for this data source
connection_idYes*UUID of the pre-configured connector (*not required for Roe Tables)
descriptionNoHelps the agent understand what data is available

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SQL Context Sources

SQL-based context sources (Snowflake, Roe Tables) enable agents to query structured data using natural language.

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Snowflake Configuration

{
  "connection_type": "snowflake",
  "name": "Transaction History",
  "connection_id": "conn_abc123",
  "table": "TRANSACTIONS",
  "description": "Customer transaction data including amounts, dates, and response codes"
}
FieldDescription
tableTable name for the agent to query
sql_query(Optional) Predefined SQL query—overrides automatic query generation

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Roe Tables Configuration

Roe Tables are stored directly in VolansDB and don’t require a separate connection. 
{
  "connection_type": "Roe Tables",
  "name": "Alert History",
  "table": "alerts",
  "description": "Historical alert data with resolutions"
}

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Agent SQL Capabilities

When configured with SQL context sources, agents can:
  1. Discover Schema: Automatically understand table structure and column types
  2. Generate Queries: Create appropriate SQL based on natural language instructions
  3. Apply Filters: Add relevant WHERE clauses based on investigation context
  4. Handle Errors: Recover from query issues and retry with corrections
Provide a detailed description to help the agent understand what data is available and when to use this source.

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API Context Sources

API-based context sources (Zendesk) enable agents to interact with third-party services.

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Zendesk Configuration

{
  "connection_type": "zendesk",
  "name": "Customer Support History",
  "connection_id": "conn_xyz789",
  "description": "Pull all support tickets for the customer"
}
The description field is crucial for Zendesk—it tells the agent what to search for:
Description ExampleAgent Behavior
"Pull all support tickets for this customer"Searches tickets by customer email/ID
"Search for tickets mentioning fraud"Searches for fraud-related keywords
"Get tickets with 'escalated' tag"Filters by specific tags

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Example: AML Investigation Setup

Here’s a complete context source configuration for an AML investigation: 
{
  "context_sources": [
    {
      "connection_type": "snowflake",
      "name": "Transaction Data",
      "connection_id": "conn_sf_123",
      "table": "TRANSACTIONS",
      "description": "Transaction history with amount, merchant, and response codes"
    },
    {
      "connection_type": "snowflake",
      "name": "Customer Profiles",
      "connection_id": "conn_sf_123",
      "table": "CUSTOMERS",
      "description": "Customer KYC data and account information"
    },
    {
      "connection_type": "zendesk",
      "name": "Support Tickets",
      "connection_id": "conn_zd_456",
      "description": "Customer support history and communications"
    }
  ]
}

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How Agents Use Context Sources

During investigation, agents follow this flow:
1

Identify Data Need

Based on the SOP or investigation requirements, the agent determines what data is needed
2

Select Source

The agent chooses the appropriate context source based on the name and description
3

Generate Query

For SQL sources, the agent generates a natural language query that’s converted to SQL
4

Fetch Data

The query is executed and results are returned to the agent
5

Analyze Results

The agent incorporates the data into its investigation and analysis

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Best Practices

Use Descriptive Names

Name context sources clearly (e.g., “Transaction History” not “Table1”)

Write Detailed Descriptions

Help agents understand when and how to use each source

Limit Table Scope

Specify specific tables rather than exposing entire databases

Use Read-Only Access

Configure connectors with minimal permissions for security

Connectors

Set up connections to your data sources

AML Investigation

Configure context sources for AML workflows