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An AI agent can query data across distributed systems through a governed catalog, approved query tools, and connectors that reach the underlying sources. That pattern can avoid copying every dataset into one store, but it does not guarantee petabyte-scale query performance. Keep large analytical workloads in a lakehouse when appropriate, federate selected remote sources, and validate each workload’s performance, cost, and authorization behavior.
How an agent can query data across multiple systems
Put a controlled query layer between the agent and enterprise data. The agent should discover available datasets and business context through approved metadata tools, then submit a validated query to a federation-capable service. Connectors access the source systems and may push filters toward them. For large analytical datasets, a lakehouse using an open table format such as Iceberg can provide a managed analytics layer; federation and ingestion can serve other workloads.
An AWS example uses Glue Data Catalog metadata and Amazon Athena tools exposed through a Model Context Protocol (MCP) interface. AWS also describes direct-source access as an alternative when source-native tools are a better fit. These are example architecture choices, not evidence that one vendor or interface is right for every environment.
- User request: The application passes a user’s question to the agent.
- Metadata discovery: The agent uses approved tools to identify relevant datasets, schemas, and business terminology.
- Query construction and checks: The application or query service validates the proposed SQL and restricts its permitted scope.
- Federated execution: The query service invokes the relevant connector or connectors to read from source systems.
- Response and records: The application returns an authorized result and records the invocation and query outcome.
Amazon Web Services’ “Use Amazon Athena Federated Query” documentation says: “When you run a query against a data source, Athena invokes the connector to determine which data to read, manages parallelism, and pushes down filter predicates.” Those mechanisms can help avoid unnecessary reads, but how much work they eliminate depends on the connector, source, and query.
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How to deploy the pattern
- Inventory sources and workloads. For each source, record its location, owner, sensitivity, freshness needs, expected query shapes, concurrency, and source-side limits. Separate large analytical datasets from operational or remote data. Decide whether each workload suits federation, ingestion, or a managed lakehouse copy; AWS architecture guidance describes choosing federation and ingestion by use case rather than applying one approach everywhere.
- Build the metadata layer. Register datasets with schemas, owners, sensitivity labels, descriptions, and business terms that help an agent distinguish similar fields. Catalog-first discovery gives the agent a consistent way to find tables and columns before querying. It also makes catalog coverage and upkeep prerequisites, which can slow onboarding of rapidly changing sources.
- Evaluate connectors individually. Verify support for each source, authentication method, SQL operation, filter pushdown, network route, concurrency limit, and integration with the catalog and governance controls you intend to use. Athena documentation distinguishes Glue Data Catalog federated connectors from Athena-specific connectors; their governance properties differ, so “Athena connector” is not by itself proof that the same policy path applies to every source.
- Expose narrow agent tools. Provide specific metadata-discovery and query-execution tools through an application boundary, such as the MCP pattern in AWS’s architecture example. Keep credentials and unrestricted service APIs out of free-form agent control. Validate generated SQL, constrain accessible schemas and query scope, and require human approval where sensitive data or costly operations warrant it. MCP enables a tool interface; it does not itself supply authorization, safe SQL generation, or audit controls.
- Connect identity to permissions. Map the requesting user’s approved identity to query and source permissions. Check access at the catalog, database, table, and column levels where supported, and verify how the connector and source enforce those decisions. A central catalog does not establish that every connector path propagates permissions identically. AWS documentation describes fine-grained controls in the lakehouse federation context, but the selected source and connector still need to be tested.
- Route data by workload. Keep suitable large analytical datasets in a managed lakehouse. Federate selected sources when current remote access is useful and the source can handle the reads. Ingest or materialize data when repeated remote queries, source constraints, or operational needs favor a managed copy. The sources do not establish a universal threshold for when one choice becomes better than another.
- Test representative workloads. Measure large scans, selective filters, cross-source joins, skew, concurrent requests, connector failures, source throttling, and realistic agent retries. Track latency, bytes scanned and transferred, source load, query cost, and policy outcomes. The documented mechanisms do not establish performance or cost guarantees for this combined architecture.
- Operate with auditability. Record user identity, agent and tool invocations, query text or a normalized form, source access, policy decisions, errors, and lineage where available. AWS architecture guidance identifies lineage and auditability as design goals; the implementation and coverage depend on the chosen stack and must be verified.
Which data-access pattern fits?
| Pattern | Useful when | Tradeoff to assess |
|---|---|---|
| Catalog-first federation | Agents need consistent metadata, semantics, and discovery before querying sources. | Catalog coverage and maintenance can slow access to new or fast-changing sources. (AWS architecture guidance) |
| Direct source access | A source’s native tools are useful and catalog onboarding is a poor fit. | Identity, governance, logging, and tool behavior may become fragmented across source-specific interfaces. (AWS architecture guidance) |
| Ingest or materialize into a lakehouse | Repeated analytical reads, stable snapshots, or workload controls favor managed data copies. | Requires data movement and adds freshness, storage, and pipeline operations to manage. (AWS architecture guidance) |
Compare candidates against the same criteria before choosing: permission enforcement and identity propagation; metadata completeness; filter pushdown and source load; freshness and snapshot behavior; cross-source joins and data movement; cost and concurrency; audit and lineage; and connector reliability and operational ownership.
What “petabyte scale” does—and does not—mean
Petabyte-scale storage describes the size of a data estate; it does not establish that a federated query across that estate will meet a particular latency, concurrency, or cost target. A lakehouse may hold very large analytical datasets, while a federated query may read from separate remote sources. Query performance and expense depend on source behavior, data layout, query shape, connector capabilities, network paths, and deployment configuration. No universal benchmark or maximum workload scale is established for this agent architecture.
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Validate the combined system with representative queries and production-like concurrency before relying on it for a workload. A selective query with effective filter pushdown is a different case from a large scan or cross-source join; results from one should not be treated as a guarantee for the others.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Governance details to verify in an AWS implementation
AWS’s Athena documentation distinguishes Glue Data Catalog federated connectors from Athena-specific connectors, including differences relevant to Lake Formation governance. It also notes that external catalogs do not support write operations and that using Secrets Manager with the federated-query feature requires a VPC private endpoint. Confirm those constraints against the current documentation and the exact connector configuration you plan to deploy; connector availability, registration, and governance integrations can change.
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