---
title: "Evaluate agent memory with warehouse data and schemas"
description: "Compare API-managed context, application-owned memory, and warehouse-native retrieval. Test permissions, correction, latency, and source recovery."
canonical: "https://scalewithsearch.com/articles/agent-memory-warehouse-custom-schema-evaluation"
---
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# Evaluate agent memory with warehouse data and custom schemas

This guide proposes an evaluation for a real estate business’s approved facts, decisions, and warehouse records. Select candidates against your own questions, access rules, correction needs, and measured response times.

A warehouse connection retrieves business data. Persistent memory retains approved facts or decisions across work sessions. Conversation storage preserves interaction state. Require a design that identifies each responsibility.

Primary documentation was checked on 2026.10.22. This is a source-based shortlist and proposed evaluation, not a performance benchmark.

## Compare three design candidates

| Design to evaluate | Responsibility to demonstrate | Evidence to require |
| --- | --- | --- |
| Hosted conversation and document services | Recover conversation state and retrieve approved source documents. | Warehouse adapter, schema validation, authorization, and correction lifecycle. |
| Application-owned persistent storage | Recover thread state and approved facts across sessions. | Warehouse adapter, source citations, access enforcement, and recovery after restart. |
| Search over an existing warehouse | Retrieve from a bounded, approved source query. | Service access, restricted-row tests, correction visibility, and a separate approved memory record. |

The [persistence framework documentation](https://docs.langchain.com/oss/python/langgraph/persistence) distinguishes short-term thread checkpoints from stores for durable information across threads. Its guide warns that in-memory checkpoint storage does not survive process restarts. [Persistence and restart limits, checked 2026.10.22](https://docs.langchain.com/oss/python/langgraph/persistence).

The [warehouse-search documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview) describes retrieval over warehouse data. Its security model uses the service owner's rights. Access to a service can expose indexed content beyond a caller's underlying-table privileges. Verify the current security section and source scope before considering it for restricted business records. [Warehouse-search overview, checked 2026.10.22](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/cortex-search-overview), [query access controls](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-search/query-cortex-search-service#label-cortex-search-query-syntax-access-controls).

No row in this table claims a ready-made universal warehouse adapter or a measured advantage.

## Own the memory schema

Define the business record before choosing the retrieval engine. A proposed record contains:

- A stable memory identifier and business or tenant identifier.
- The approved fact or decision and its source reference.
- Source revision, approval date, and responsible person.
- Status, expiry rule, and any superseded record.
- The access policy and deletion state.

Validate those fields before a record becomes eligible for retrieval. Keep the complete record in your controlled database or files. Put only suitable search fields in an index. Keep search metadata separate from the complete approved record.

## Benchmark answers and boundaries together

Build a synthetic evaluation set with current facts, corrected facts, missing answers, and deliberately restricted records. Use the same source revisions and questions for every candidate.

Measure supported-answer accuracy, citation correctness, correction visibility, denied-record exposure, and latency. Record cold and repeated requests separately. Define the workload and acceptance thresholds before running it.

For warehouse questions, require the query, approved role, data snapshot, and result time. A fast answer without the right access boundary fails the proposed evaluation.

## Make the decision from evidence

Keep adapter compatibility, account permissions, operating costs, and workload performance unresolved until tested. If the business already has suitable storage, first test whether it can remain authoritative.

The selected system should recover the approved record after a restart, apply corrections, refuse unauthorized retrieval, and export usable source records. Preserve the evaluation results alongside those records.

For the distinction between retrieval and business decisions, read [RAG versus business memory](/articles/rag-vs-business-memory).

Bring the source inventory, access requirements, and evaluation results to your [real estate website brief](/for/real-estate). Request a written scope through [Scale With Search’s work request](/work).

- [Add AI to a system of record without duplicating authority](/articles/ai-system-of-record-without-second-stack)
- [Why an AI response restarts after reconnecting](/articles/ai-response-restarts-after-reconnect)
- [Buyer agreement pages for real estate websites and AI comparisons](/articles/buyer-agreement-page-for-ai-agent-comparisons)


## Related: Real estate

- [Add AI to a system of record without duplicating authority](/articles/ai-system-of-record-without-second-stack)
- [Why an AI response restarts after reconnecting](/articles/ai-response-restarts-after-reconnect)
- [Buyer agreement pages for real estate websites and AI comparisons](/articles/buyer-agreement-page-for-ai-agent-comparisons)

----

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Scale With Search  2026  [scalewithsearch.com](https://scalewithsearch.com)
```
