Use Case

Agents Querying Warehouses

Adaptive scopes warehouse agents to specific schemas and row-level policies — review every query, redact sensitive fields, revoke instantly. You write the prompts and workflows; Adaptive provides the harness, tools, MCP registry, networking, and guardrails.

harness·h-5511

Adaptive

Query

Analyst

Dataset

Rows

Access

q_rev_q1

alice

orders

1.2M

view

q_churn

bob

users

480K

view

q_pii_dump

carol

customers

—

blocked

pii: masked

row-level: on

session: recorded

The problem

Data and analytics agents need to query warehouses like Snowflake, BigQuery, and Redshift to generate insights. But broad query access means agents can access sensitive tables, join across datasets they shouldn't see, and exfiltrate data through unmonitored query results.

84%

of data warehouse environments have overly permissive access policies that extend to AI agents

12x

increase in query volume when AI agents are given unrestricted warehouse access, amplifying data exposure risk

39%

of organizations have experienced data leakage through AI agent queries accessing sensitive warehouse tables

Data warehouses aggregate sensitive data from across the organization. Without per-agent scoping, a single compromised or misconfigured analytics agent can access customer PII, financial records, and proprietary business data.

The solution

Schema-level scoping and query-level controls for analytics agents

Adaptive provides the harness, tools, MCP registry, networking, and guardrails — schema-level access policies, row-level security, query budgets, and field redaction for every analytics agent session. You provide the prompts and workflows. The agent runs your analysis inside Exo policy envelope; every query is logged for review.

Benefits

How Adaptive helps

Schema-Level Scoping

Restrict agents to specific schemas, tables, and columns. Prevent cross-schema joins and access to tables outside the agent's defined scope.

Write the prompts and workflows that drive the agent. Exo enforces per-agent schema policies that map to your data classification levels — the workflow you authored only sees the tables and columns it was scoped to.

Row-Level Policies

Apply row-level security policies that filter query results based on the agent's context — team, project, or customer scope.

Configure row-level filters that automatically apply to every agent query, ensuring data isolation without application-level changes.

Query Budget Controls

Set query frequency limits, result size caps, and compute budgets per agent. Prevent runaway queries and data exfiltration through large result sets.

Define query budgets per agent role — limit scan volume, result row counts, and concurrent query slots.

Sensitive Field Redaction

Automatically mask PII, financial data, and other sensitive fields in query results. Agents get the analytical signal without raw sensitive values.

Apply column-level masking policies that redact sensitive fields while preserving analytical utility for aggregations and trend analysis.