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World desk7 min

Giving a Satellite-Analysis Agent a Memory: What to Store, and When to Revalidate It

A satellite-analysis agent needs memory of its analytical state, not only chat history. Here is what to store, why geospatial results go stale, and how to compare options.
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A satellite-analysis agent needs memory that records what it computed, not only what the user asked. A follow-up such as “how did the flooded area change since the last pass?” is only answerable if the agent can recover which scenes, area of interest, coordinate reference system, and processing steps produced the earlier result, and can tell whether that earlier result still applies to the new question. Memory that stores only conversation text cannot do this reliably.

Why the context window is not the memory

An agent’s context window is temporary working material. It holds the current prompt, recent messages, and tool outputs for the duration of a session or task. It is not a durable record. The July 2026 informational Internet-Draft Architecture and Data Model for Persistent Memory in Agentic Systems (dated July 19, 2026, with a stated expiry of January 20, 2027) makes this point directly: “A model context window is not the authoritative memory record.” The draft is a proposal, not a settled standard, so treat its terms as a design vocabulary rather than a compliance requirement.

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Persistent memory, by contrast, is stored state that can be addressed and retrieved in later operations. For a satellite-analysis agent it needs four properties that a chat transcript lacks: a defined scope (which user, project, or investigation it belongs to), provenance (where each fact came from), a lifecycle (how it is superseded, corrected, or expired), and a policy for retention and review.

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What a satellite-analysis memory record should hold

Earth-observation work is analytical, so the memory should preserve the analysis as well as the conversational intent behind it. The fields below are an editorial synthesis of the draft’s provenance and state model and of the requirement in the 2026 EO position paper that agents operate on structured geospatial state. They are a starting schema, not a published specification.

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  • Investigative question: the user’s stated goal, so later follow-ups can be read in context.
  • Area of interest: the geometry as stored, with its coordinate reference system, not only a place name.
  • Observation window: acquisition dates or date range, and the time zone or convention used to define them.
  • Sensor and modality: platform, instrument, band set, and modality such as optical, SAR, or thermal.
  • Dataset and scene identifiers: the exact product IDs or catalog references used, not a general description.
  • Processing lineage: each step in order, with tool name, version, and parameters (for example, resampling method, cloud-mask threshold, compositing rule).
  • Tool inputs and outputs: enough to reproduce or audit the step, or a reference to where those artifacts are stored.
  • Derived result: the number, map, or classification produced, linked to the lineage above.
  • Evidence links: references to the source scenes and output artifacts that support each claim.
  • Assumptions and uncertainty: stated limits, such as a cloud-cover caveat or a model’s known error range, recorded when the result was produced.

Why geospatial state goes stale faster than text

A remembered sentence such as “the reservoir was at 62 percent capacity” does not change when the underlying data changes. A remembered raster-derived result does. The EO position paper by Munir et al. (arXiv, April 27, 2026) makes this its central warning: “Building reliable geospatial agents therefore requires rethinking agent design around the physical, geospatial, and workflow constraints that govern EO analysis.” The paper argues that a coherent answer is not enough. Correctness also depends on geospatial consistency, temporal validity, and physical validity.

Several operations change the analytical state in ways that a later step may not notice:

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  • Reprojection moves pixels onto a different grid. Areas and distances computed before and after may differ, and two layers reprojected differently cannot be overlaid without care.
  • Resampling changes spatial resolution. A value computed at 10 m and later compared with one at 30 m may describe different things.
  • Compositing combines many scenes into one image, for example a median composite over a month. The composite has no single acquisition date, so a memory that stores only a single date is wrong.
  • Aggregation summarizes pixels into zonal statistics or counts. The aggregated number cannot be disaggregated again from memory alone.

Consider an illustrative case, not a measured one. An agent computes a flood extent last week from a median composite that was resampled to 30 m, then a user asks for a comparison with a new single-scene analysis at 10 m. If the memory holds only the final hectare figure, the agent will compare the two numbers as if they were equivalent. The error does not announce itself; it appears as a plausible change. This is the failure mode that makes memory for geospatial agents different from memory for text agents. A plausible memory is not automatically a valid memory for a new analysis.

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Derived objects help retrieval, but they are not the truth

The draft treats embeddings, indexes, graph projections, and summaries as derived state. That distinction matters more here than in most applications. An embedding of a paragraph describing a flood map helps the agent find it, but it cannot tell the agent which scenes produced the map. A summary that says “flooding was extensive in the eastern district” may be wrong about extent if the summary was written before a reprocessing step.

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The practical rule is to keep source observations and analysis outputs as versioned records, and to build indexes from them. If an index is lost or corrupted, it can be regenerated. If the authoritative record is lost, the memory is gone.

Comparing implementation approaches

Compare candidate stores on the axes below. They are derived from the documented designs in the vendor material and the draft, not a ranking.

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  • Geospatial and temporal model: can it represent geometries, time ranges, sensors, coordinate systems, and changing observations?
  • Provenance and audit: can a reader trace a result to the scenes, tools, parameters, and memory updates that produced it?
  • Retrieval: does it support exact identifier lookup, lexical search, semantic retrieval, and relationship traversal?
  • 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 only text?
  • Operational fit: consider deployment model, data locality, integrations, cost, and whether your team can maintain it.

The three vendor products below illustrate how these axes differ. Vendor documentation describes features; it is not independent testing, and none of these were evaluated here against a satellite workload.

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Axis SurrealDB (vendor documentation) Couchbase Agent Memory (vendor documentation) Cloudflare agent memory (vendor documentation, June 2, 2026 update)
Data model Graph, vector, document, relational, and geospatial records Session-spanning memory blocks, facts, embeddings, and timestamps Scoped memory profiles
Provenance Record-level provenance Timestamps recorded; provenance detail not stated Not stated
Retrieval Persisted retrieval traces Embedding-based memory retrieval Recall across agent executions
Scope Not stated Not stated Scoped profiles
Lifecycle Not stated Not stated Automatic or explicit memory ingestion
Non-text modality Geospatial records, but imagery handling not stated Does not support non-textual content Not stated
Reasoning logic Not stated Does not provide reasoning logic Not stated
Availability Not stated Not stated Private beta as of the June 2, 2026 documentation update

Two readings follow from the table. A store with strong geospatial and provenance features still needs your own rules for revalidation, because it will not decide on its own that a resampled composite is no longer comparable. A text-oriented memory service can hold the investigative narrative, but imagery and scene references must live in a separate, authoritative store.

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A recall pattern that resists silent error

The following sequence is a synthesis of the draft’s architecture and the workflow constraints in the EO position paper. It is a design pattern, not a tested implementation.

  1. Write each source observation and each analysis output as a versioned record that carries the scope, provenance, and lineage fields described above.
  2. Build retrieval indexes (keyword, vector, or graph) from those records. Store index entries as derived objects that point back to the record ID.
  3. On a follow-up, retrieve candidate records by scope and relevance, not by the most recent summary.
  4. For each candidate, check whether its observation window, area of interest, coordinate reference system, modality, and processing lineage match the new request. Reject or convert candidates that do not match.
  5. Give the agent the record and its source references, not only a summary. The agent should be able to show the scenes and parameters behind a claim.
  6. If a remembered claim is time-sensitive, or if a source product has been reprocessed since the claim was made, mark the record for revalidation instead of presenting it as current.

Checks to run before trusting recalled results

  • The recalled result’s observation window matches the requested window, or the difference is stated in the answer.
  • Both results use the same coordinate reference system and resolution, or the agent reprojects and resamples them with recorded parameters before comparison.
  • Composite results are never compared with single-scene results without an explicit note.
  • A source product’s version or processing baseline has not changed since the memory was written.
  • Derived summaries are checked against their source record before they are quoted.
  • Evaluation covers the intermediate steps, not only the final prose. The EO position paper calls for trajectory-level evaluation for this reason.

What is and is not established

The 2026 WACV GeoCV survey places memory, retrieval, tools, and evaluation within an Earth-observation agent stack and identifies shallow temporal memory as a limitation of current systems. The July 2026 Internet-Draft proposes typed and versioned memory objects and an append-only event ledger, but it is an informational draft that may change or lapse. The EO position paper is an arXiv preprint dated April 27, 2026. None of these sources establishes a particular product’s memory performance on satellite tasks.

SatQuery AI is described in exact-title search results as a conversational platform for satellite and Earth-observation analysis that aims to support “an ongoing geospatial investigation through natural-language follow-up questions.” The original Reddit discussion could not be reviewed, so the description above is search-result language. How that platform implements memory is not verified here, and the design advice in this article does not depend on it.

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Start with the authoritative record, keep the index derived, and make revalidation part of recall. Those three choices matter more to the reliability of a follow-up answer than the particular database you pick.

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