The Tool Desk
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Does Vertex AI remember previous conversations?
Not simply because a conversation was sent to a model. A model’s context window is the working information available for a request; it is not, by itself, a durable store that an agent can query in a later session. Google’s long-context guidance compares the context window to short-term memory and describes summarization, retrieval-augmented generation (RAG), and filtering as ways to manage limited context.
For continuity, your application needs to manage conversation state and, when the agent must recall information across sessions, save it to a persistent memory or retrieval resource. At answer time, the application can load relevant state or retrieve relevant memory and include it in the model’s working context. A larger context window can hold more material for a request, but does not make that material durable or define how it should be updated, sourced, or expired.
What is the difference between session state, persistent memory, and context?
| Layer | What it holds | How the agent uses it | What it does not guarantee |
|---|---|---|---|
| Session and state | Messages, tool results, and variables needed to continue an interaction. | The application or agent accesses the active session’s state while handling that chat. | Cross-session recall or a policy for retaining information between chats. |
| Persistent memory or RAG resource | Selected facts, conversation memories, or indexed source material saved for later retrieval. | The application retrieves relevant items and supplies them to the model when needed. | That generated or retrieved information is true, current, complete, or appropriate to use. |
| Model context or service-side cache | Information in the current model request, or data cached for a documented service behavior. | Supports generating a response or, for specific features, latency or session resumption. | General-purpose, application-controlled long-term memory. |
Google’s Agent Development Kit (ADK) describes a Session and its state as short-term memory for one chat. That can carry the thread forward, including tool outputs, but it is distinct from a configured memory service intended to support recall across sessions. Your application’s persistence and lifecycle choices determine what happens to session records.
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Which Vertex AI memory approach should you choose?
| Need | Starting point | Main trade-off |
|---|---|---|
| Keep messages, tool results, and workflow variables available during one chat | ADK session and state | Convenient short-term interaction state; not a cross-session memory policy. |
| Recall a concise, evolving set of facts extracted from conversations | Vertex AI Agent Engine Memory Bank | Memories are generated and consolidated, so provenance, correction, scope, and expiration need deliberate handling. |
| Find relevant passages in transcripts or other indexed material | ADK’s VertexAiRagMemoryService or a RAG corpus |
Returns retrieved source content rather than only a consolidated fact; relevance scores depend on the configured metric. |
| Work with more information than is useful to place in a prompt at once | Summarization, filtering, or RAG | Requires deciding what to retain or retrieve; expanding context alone does not create durable storage. |
The right choice depends on what should be saved, how it should be retrieved, whether the original source must be shown, how corrections and contradictions are handled, and how long the information should remain available. Infrastructure, response latency, and cost are also practical evaluation criteria; the cited product documentation does not establish a universal winner on those measures.
Use Memory Bank for selected, consolidated memories
ADK describes Memory Bank as extracting meaningful information from conversations and consolidating it with existing memories. This is a fit when an agent needs a smaller set of useful facts rather than a search through entire transcripts on every turn. The stored result is still derived from conversation: extraction is not proof that a statement is accurate, and a changed preference or disputed fact needs an application-level correction policy.
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The Vertex AI Agent Engine Memory Bank API reference documents configuration for memory generation, similarity search, automatic time-to-live (TTL), and memory revisions. It specifies text-embedding-005 as the default embedding model for Memory Bank similarity search when another model has not been set. TTL is configurable; if automatic TTL is not used, expiration can be managed through each memory’s expire_time. The API reference retrieved on October 4, 2026 labels Memory Bank as Preview, so check the current release stage and availability for your intended region before relying on it in production.
Use RAG-backed memory for source-bearing passages
ADK’s VertexAiRagMemoryService stores conversations in Knowledge Engine and retrieves them by vector similarity. ADK positions it for raw conversation retrieval or retrieval alongside other RAG-indexed content. This is useful when the agent should be able to bring back a passage from a conversation or corpus, rather than relying only on a generated summary.
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Vertex AI RAG context retrieval accepts a text query and can return relevant contexts with fields such as source URI or display name, text, and a score. The Vertex AI RAG quickstart uses text-embedding-005 in its example; that is an example implementation, not a requirement for every corpus or workload. RAG may support dense and sparse hybrid ranking, with an alpha parameter controlling their weighting. The right setup depends on the indexed data and retrieval configuration.
How do vector embeddings retrieve a memory?
- Represent content as vectors. An embedding model maps text—such as a saved fact, a conversation passage, or a user query—to a numeric vector intended to capture semantic features.
- Index the saved items. A memory or RAG system keeps those vectors associated with the stored facts or source chunks. The original text or source metadata is needed if the agent must present or ground an answer in the retrieved material.
- Embed the current query. The query is represented in the vector space used for retrieval.
- Rank candidates within the applicable search scope. The system compares the query vector with stored vectors using its configured database and metric, then returns selected items or passages.
- Supply the result to the model. The application places retrieved information into the request context so the model can use it to answer. Retrieval makes information available; it does not itself certify that information as true.
Do not assume a similarity score is a probability or that a larger number is always better. The Vertex AI RAG API says score interpretation depends on the underlying vector database and metric. In its cosine-distance example, a greater distance means less relevance. Determine whether a configured score is a distance or similarity and how that metric ranks results before setting thresholds or displaying scores to users.
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Memory Bank scopes are part of the data model
Memory Bank retrieval is constrained by scope: a memory is returned only when the requested scope exactly matches its scope, including the same keys and values with case sensitivity. A memory’s scope is immutable after it is generated or created. Similarity search then compares the request with embeddings of memory facts inside that scope.
Design scopes deliberately—for example, around the identity or tenant boundary that should be allowed to retrieve a memory. Because scope cannot be edited in place, changing the intended boundary may require creating memory under a new scope and managing the old records through your application’s migration and deletion process.
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How should an agent track what it knows?
“Epistemic state” is a useful design lens, not a named Vertex AI feature in the cited documentation. It means keeping track of what the application treats as known, what is uncertain, which source supports a claim, and when that claim may be stale. Memory generation, vector retrieval, and TTL do not by themselves provide this judgment.
For durable information that could affect an answer or action, preserve provenance alongside the claim when possible: where it came from, when it was recorded, and whether it was user-stated, inferred, or retrieved from a source. Choose update rules for changed facts, contradictory statements, and claims whose validity declines over time. These are application design recommendations, not built-in guarantees of Memory Bank or RAG.
- Keep the source: Prefer a source-bearing passage or a reference to the original record when a claim needs verification.
- Represent uncertainty: Avoid turning an inference or an unconfirmed statement into an unqualified fact.
- Handle change explicitly: Decide whether a newer statement replaces an older one, needs confirmation, or should remain a visible contradiction.
- Set a retention rule: Use automatic TTL or per-memory expiration where appropriate, and define review or removal policies for facts whose accuracy can change.
What does “ephemeral” mean for Vertex AI context and caching?
Use “ephemeral” only with a named feature and its documented retention behavior. There are three different lifetimes to consider: the context assembled for a model request, a service-side cache used for a particular behavior, and application-controlled resources such as sessions, Memory Bank memories, or a RAG corpus. They are not interchangeable.
Google Cloud’s zero-data-retention documentation says published Gemini models cache customer inputs, outputs, and derived data in project-isolated in-memory caches by default to reduce latency, with a 24-hour TTL. The same documentation describes Gemini Live API session resumption as disabled by default and requiring the user to enable it on a request; for that feature, cached prompts and outputs can be retained for up to 24 hours to allow a session to resume. It also notes a Grounding with Google Maps exception to disabling storage. These are descriptions of specific documented behaviors, not a general retention promise for every Vertex AI feature or customer data.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteIf retention matters for your application, identify the exact model and feature, review its current data-handling documentation, and separately establish how your own session store, memory resource, and indexed corpus retain or delete information. A cache lifetime does not replace those application-level controls.
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