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How the architecture works
The agent should not receive general-purpose database credentials or unrestricted control of service APIs. Instead, it calls a small set of approved tools through an application boundary. One tool discovers metadata; another submits a validated query to a service that coordinates reads from the relevant sources.
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- Discover: The agent searches a governed catalog for datasets, schemas, ownership, sensitivity labels, and business descriptions.
- Form and validate: It drafts a query using the discovered schema. The application checks the query’s allowed sources, scope, and sensitivity before execution.
- Execute: A federation-capable query service invokes connectors to read the relevant data. Connectors may push filters toward sources, reducing unnecessary reads when the source and connector support that operation.
- Return and record: The application returns only the authorized result and records the identity, tool activity, query, policy outcome, and relevant source details.
In an AWS example, Glue Data Catalog metadata and Amazon Athena discovery and query tools can be surfaced to an agent using the Model Context Protocol (MCP). AWS also describes direct access to source-specific tools as an alternative. MCP provides a tool interface; it does not, by itself, establish authorization, safe SQL generation, or complete audit coverage.
Choose federation, direct access, or ingestion by workload
These patterns can coexist. A lakehouse may hold large analytical datasets, while federation serves selected remote or operational sources and ingestion serves workloads that benefit from a managed copy. Choose per dataset and access pattern rather than imposing one method on every source.
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| Pattern | Useful when | Tradeoff to assess |
|---|---|---|
| Catalog-first federation | Agents need consistent metadata and business context before querying multiple sources. | Catalog coverage and upkeep can delay onboarding or make fast-changing sources harder to discover. |
| Direct source access | A source has useful native tools and catalog onboarding is a poor fit. | Identity, governance, logging, and tool behavior may vary across source-specific interfaces. |
| Ingest or materialize into a lakehouse | Repeated analytical reads, stable snapshots, or workload controls favor a managed copy. | Requires data movement and adds freshness, storage, and pipeline operations to manage. |
Compare candidate designs on identity propagation and permission enforcement; metadata completeness; predicate 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. These are workload and governance questions, not properties guaranteed by the word “federated.”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Deploy the governed agent data layer
1. Inventory sources and workloads
For each dataset, record where it lives, who owns it, its sensitivity, freshness needs, query patterns, expected concurrency, and source-side limits. Classify it as lakehouse analytics, a suitable remote source for federation, or a candidate for ingestion or replication. AWS architecture guidance allows federation and ingestion choices by use case; it does not establish a universal threshold for choosing between them.
2. Build useful catalog coverage
Register datasets with schemas, descriptions, owners, sensitivity labels, and business terminology. Catalog-first discovery helps an agent identify available tables and columns before it forms a query. The tradeoff is operational: incomplete or stale metadata weakens discovery, and catalog onboarding can slow access to sources that change rapidly.
The Tool Desk
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For every connector, check source support, authentication, network path, supported SQL operations, predicate pushdown, concurrency limits, and integration with the intended catalog and governance services. Do not assume connectors have equivalent capabilities. Amazon Athena documentation describes Athena invoking connectors, managing parallelism, and pushing down filter predicates; it also distinguishes Glue Data Catalog federated connectors from Athena-specific connectors, which have different governance properties.
Confirm current compatibility and policy behavior for the precise connector and source you plan to use. Athena documentation also notes limitations relevant to configuration: external catalogs do not support write operations, and using Secrets Manager with the federated-query feature requires a VPC private endpoint. Verify these requirements against the current documentation and your intended setup.
4. Expose narrow, controlled agent tools
Put metadata discovery and query execution behind an application boundary, such as the MCP-based tool flow described in AWS architecture guidance. Keep service credentials and unrestricted APIs out of the agent’s free-form control. Validate generated SQL, constrain accessible schemas and query scope, and require human approval where the sensitivity or cost of an operation warrants it. These are deployment controls to implement and test, not automatic guarantees supplied by MCP.
5. Tie authorization to the caller
Propagate or map the user’s approved identity through the application and query path, then verify permissions at the catalog, database, table, and column levels where supported. Test what happens at the connector and source boundary as well as in the catalog. A centralized catalog does not prove that every connector enforces policies identically; connector type and source behavior matter.
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6. Route data to the right serving layer
Keep large analytical datasets in a managed lakehouse when their access pattern suits it. Use federation for remote sources whose access characteristics make on-demand reads appropriate. Ingest or materialize data when repeated remote reads, source constraints, freshness requirements, or operational needs favor a managed copy. The right choice depends on the workload; the available architecture guidance does not supply a universal cutoff.
7. Test representative workloads before scaling out
Documentation about federation mechanisms is not a performance guarantee for an agent architecture. Test the combination of agent, query service, connector, source, network, and data layout with realistic requests, including:
- Large scans and selective filters, to measure bytes scanned and transferred and whether filters are pushed down.
- Cross-source joins and skew, to understand data movement and the effect on query time.
- Concurrent requests, connector failures, source throttling, and realistic agent retries.
- Policy checks for allowed and denied data, including behavior when a connector or source cannot enforce an expected control.
Measure latency, query cost, source load, concurrency behavior, and policy outcomes. There is no universal latency target, cost model, or maximum workload scale established for this pattern; results depend on source behavior, query shape, data layout, and deployment configuration.
8. Audit and operate the full path
Record user identity, agent and tool invocation, query text or a normalized query, source access, policy decisions, errors, and lineage where available. Define who owns connector health, credential rotation, catalog updates, incident response, and reviews of access. Auditability and lineage are architecture goals; confirm that the selected services and connectors actually provide the coverage your requirements demand.
What “petabyte scale” does—and does not—mean
A lakehouse can be designed to store petabyte-scale analytical data, while federation can provide access to data in other systems without first copying every source into that lake. Those facts do not mean that an arbitrary federated query over petabyte-scale data will be fast, inexpensive, or feasible. Query performance and cost depend on such factors as how much data a query reads, the source’s capacity, connector behavior, filter pushdown, join patterns, concurrency, and network transfer.
Use petabyte scale to shape capacity planning and workload testing, not as a service-level guarantee. Validate each intended query class under realistic conditions, then decide which data should remain remote and which should be ingested or materialized.
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