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A satellite-analysis agent that remembers only its chat history will eventually answer a follow-up with the wrong scene, an expired date range, or a figure computed in a different coordinate system. Durable memory for this kind of agent has to preserve analytical state, meaning the area studied, the observations used, the transformations applied, and the conclusion reached. It also has to mark that state as possibly out of date when its inputs change.
Where the use case comes from
An exact-title search result describes SatQuery AI as a conversational platform for satellite and Earth-observation analysis that supports “an ongoing geospatial investigation through natural-language follow-up questions.” That wording describes the workflow the memory has to serve. The original Reddit discussion could not be reviewed, so nothing in this article describes how that product implements memory. The guidance below applies to any agent with this workflow.
The context window is not the memory
The context window is the working material a model can see during one run. It is temporary. Persistent memory is separate: it is stored state that can be addressed and retrieved in later sessions, and it carries its own scope, provenance, lifecycle, and policy rules. The informational Internet-Draft Architecture and Data Model for Persistent Memory in Agentic Systems (published July 19, 2026) states the point directly: “A model context window is not the authoritative memory record.” The draft proposes typed, versioned memory objects and an append-only event ledger, and it treats embeddings, indexes, graph projections, and summaries as derived rather than authoritative. It is a draft that expires January 20, 2027, not a settled standard.
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Storing the user’s question is not enough. A follow-up such as “how much did the reservoir change since spring?” can only be answered correctly if the earlier analysis is recorded with enough detail to check whether it still applies. The field list below is an editorial synthesis of the draft’s provenance model and the call for structured geospatial state in the arXiv position paper Agentic AI for Remote Sensing: Technical Challenges and Research Directions (Munir et al., April 27, 2026).
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- Area of interest: stored as a geometry in a named coordinate reference system, not only as a place name.
- Observation window: the acquisition times of the scenes used, not the time the question was asked.
- Sensor and modality: platform, instrument, and whether the data is optical, radar, or another type.
- Coordinate reference system and grid: as delivered by the provider and as used in the analysis.
- Scene or dataset identifiers: the provider’s product IDs, so the exact inputs can be re-fetched.
- Processing lineage: ordered steps, tool versions, and parameters such as cloud-mask thresholds or resampling method.
- Tool inputs and outputs: references to stored artifacts, not only prose descriptions.
- Derived result: the number, class count, or map, with units and the statistic used.
- Evidence links: pointers to the source scenes and output files that support the result.
- Assumptions and uncertainty: for example, “cloud cover not masked in tile A,” or a stated confidence level.
- Investigative question: the original question, so later follow-ups can be matched to it.
Why geospatial state goes stale
A plausible memory is not automatically a valid one for a new analysis. The 2026 survey of geospatial computer-vision work presented at WACV’s GeoCV workshop places memory, retrieval, tools, and evaluation in an Earth-observation agent stack, and it identifies shallow temporal memory as a limitation. Remote-sensing memory fails in four ways that are easy to miss in text-only systems.
- Time: a result from a June scene does not describe a September question unless the agent checks the acquisition dates and the season.
- Geography: a stored area may only partly overlap a new boundary, or be defined in a different coordinate reference system.
- Modality: an optical-derived water mask is not interchangeable with a radar-derived one, even for the same place and date.
- Lineage: reprojection, resampling, compositing, and aggregation each change the analytical state that later steps depend on.
The lineage point is the one that breaks silently. A median composite built from three months of scenes is not an observation from any single date. If an agent later compares it against a single-date scene, the number may look reasonable while the comparison is invalid. The arXiv position paper makes the broader argument that reliable geospatial agents need design “around the physical, geospatial, and workflow constraints that govern EO analysis,” not only coherent reasoning.
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Keep observations separate from derived retrieval layers
Embeddings and summaries help the agent find memories, but they should never be the only copy of a fact. A workable pattern is:
- Write source observations, such as scene identifiers and acquisition times, as immutable versioned records.
- Write each analysis output as a record that points to its exact inputs and processing chain.
- Record corrections and changes as new events that supersede earlier records. Do not overwrite them, because the earlier value may still have been used in a report.
- Build vector embeddings, keyword indexes, or graph projections from these records.
- Treat every derived index as rebuildable. If it is lost or corrupted, the authoritative records should regenerate it.
Comparing implementation approaches
Compare candidate stores on these axes before choosing one:
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- Geospatial and temporal model: can it represent geometries, time ranges, sensors, coordinate systems, and observations that change over time?
- Provenance and audit: can a reader trace a result back to the scenes, tools, parameters, and memory updates that produced it?
- Retrieval: does it combine exact identifiers, lexical search, semantic retrieval, and relationship traversal where needed?
- Scope and access: can it isolate organizations, projects, users, and investigations?
- Lifecycle: can facts be superseded, corrected, expired, or deleted without confusing old and current state?
- Modality: does it store or reference imagery and other non-text data, or is it text-only?
- Operational fit: consider deployment model, data locality, integrations, cost, and whether your team can maintain the store.
The table summarizes what each vendor documents. These are product claims, not independent benchmarks, and the dates shown are the dates of the vendor material that was reviewed.
| Option | Documented capabilities | Documented limits | Non-text (imagery) support |
|---|---|---|---|
| SurrealDB | Graph, vector, document, relational, and geospatial records; record-level provenance; persisted retrieval traces | Not stated in the reviewed material | Geospatial records documented; storage of imagery not stated (vendor documentation, date not stated) |
| Couchbase Agent Memory | Session-spanning memory blocks, facts, embeddings, and timestamps | The service does not provide reasoning logic (vendor documentation, date not stated) | Not supported; the vendor says non-textual content is not supported (vendor documentation, date not stated) |
| Cloudflare agent memory | Scoped profiles; automatic or explicit memory ingestion; recall across agent executions | Private beta as of the June 2, 2026 documentation update | Not stated (vendor documentation, June 2, 2026 update) |
A text-only store can still be useful for satellite work if it holds references to scene identifiers and output artifacts, with the imagery kept in a geospatial data store. The main question is whether the memory layer can link to the data that gives a result its meaning.
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Recalling memory without silent reuse
When a follow-up arrives, the agent should not paste the stored summary into its answer. Instead it should run this sequence:
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- Retrieve candidate records by scope first, such as tenant, project, or investigation, and only then by relevance.
- Compare each record’s observation window with the window in the new question.
- Check geography: does the stored area match, contain, or only partly overlap the new boundary, and are the coordinate systems compatible?
- Check modality and lineage: is it the same sensor and the same processing chain, or is the difference flagged?
- Return the record together with its source references, so the answer can be traced to specific scenes and parameters.
- If a claim is time-sensitive or its source data has changed, mark it as needing revalidation and rerun the analysis instead of presenting it as current.
Scope, lifecycle, and retention
Memory should be isolated to the right user, project, tenant, or investigation, and each record should carry retention and review rules. Records that were derived from licensed or restricted imagery may need deletion rules that differ from those for user questions. Lifecycle rules should answer three questions for every record: who can change it, what it supersedes, and when it must be reviewed or removed. Deletion should remove the derived indexes too, not only the primary record.
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Evaluating the workflow, not only the answer
A fluent final answer can hide a bad intermediate step. Evaluate the trajectory: which scenes were selected, whether cloud masking was applied, whether the coordinate system was preserved, and whether the agent flagged mismatches before reusing a memory. A useful review method is to take a follow-up question with a known earlier analysis, replay the recall, and check whether the agent raised each time, geography, modality, or lineage mismatch that applies. The arXiv position paper calls for this kind of trajectory-level evaluation.
The design standard that follows is simple: store what was observed, how it was processed, and what it was used to conclude, and make the agent prove that a remembered result still matches the new question before it reuses it.
Quick Recap
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