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AI agents can see only what their configuration and delegated permissions make reachable—but that may extend far beyond the text you type. An agent may receive your instructions, search connected apps, read uploaded or mounted files, call APIs, run commands, and send results to another service. What it stores, how long it keeps that information, and where data goes next depend on the product, feature, administrator settings, and your own infrastructure.
The four boundaries that answer the question
“What data can AI agents see, store, and share?” has no single answer for every agent. Treat an agent as a configured system rather than as a fixed kind of software. Its data boundary is created by the tools it can call, the permissions attached to those tools, the environment in which it runs, and the services that receive its output.
1. Permission: what the agent can reach
An agent may be limited to the conversation, or it may have delegated access to mail, calendars, documents, databases, search indexes, cloud storage, business systems, or web services. The effective scope comes from user consent, service-account roles, administrator policies, API scopes, and tool definitions. Enabling an app does not automatically grant access to every record in that app; the agent normally inherits the identity and permissions used for the connection.
OpenAI’s documentation for ChatGPT agent in Enterprise and Edu says workspace owners can enable or disable agent mode and control which apps are available. An app unavailable to agent mode cannot be reached through that mode, although the same data might still be available through separately enabled ChatGPT features such as chat or deep research. Connected Google apps are subject to the Google account’s permissions and workspace settings.
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2. Processing: what is placed into model context
The model may process much more than the original prompt. Retrieved documents, search results, database rows, uploaded files, command output, browser pages, and tool responses can be added to the context used for inference. Microsoft describes this as augmenting a prompt with retrieved information before sending it to the configured model endpoint.
Processing is not the same as training. A provider can process content to answer a request, retain logs for security, and save state for a feature while separately stating that the content is not used to train generalized models. Each statement applies only to the product and data category it names.
3. Persistence: what remains after the run
“Stored” can mean several separate copies: a provider’s abuse-monitoring log, an uploaded file, a conversation or thread, a durable session, a tool’s result, a memory summary, a worker’s local filesystem, or a record written by a downstream application. Each copy can have a different owner, retention period, deletion path, and geographic location.
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A tool call can send input to a connected service and return its response to the agent. Publishing an agent to a collaboration platform can cause that platform to process and store the conversation. A search-grounding service, MCP server, function endpoint, or customer-managed worker may therefore receive data even when the original model provider does not retain it for the same period.
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A practical data-flow model
The following sequence is a useful way to map a particular agent. Not every product has every stage.
- Agent input: the user’s instruction, conversation history, system instructions, and any files explicitly supplied.
- Authorized retrieval: connected applications, APIs, search, databases, file stores, browsers, or other tools return information within their configured permissions.
- Execution environment: an optional shell, filesystem, package set, mounted directory, browser, network connection, or MCP tool lets the agent inspect or change data outside the prompt itself.
- Model processing: user content and retrieved material are assembled into prompts and sent to a configured inference endpoint.
- Persistence: sessions, threads, uploaded files, logs, tool outputs, memory summaries, and worker copies are saved according to separate policies.
- Response or onward action: the result is shown to the user or sent to another service to publish, update, notify, export, or trigger an operation.
What an agent may see
Prompt and conversation content
Anything in the current instruction and conversation can be processed, including pasted secrets, personal information, and text quoted from other people. Earlier turns may remain available while a session is active, and a durable session may preserve them for later turns.
Uploaded and retrieved data
Files uploaded for document analysis, records returned by a connected application, search results, and data supplied by a function or MCP server can all become model input. Microsoft lists prompts and generated content, customer-uploaded data for File Search, Code Interpreter, and Azure AI Search, external tool outputs, stateful Threads, Messages and Runs, and augmented grounding data among the information processed by its Agent Service.
Runtime and mounted data
A sandbox can expand visibility substantially. OpenAI’s sandbox documentation describes an isolated Unix-like environment that may include a filesystem, shell, installed packages, mounted data, exposed ports, snapshots, and controlled external-system access. A command running in that environment can read files or produce output that is then returned to the model. Mounting a directory therefore grants a broader boundary than uploading one selected file.
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Credentials should be injected by the runtime or provider rather than written into model instructions. Instructions are data the model can repeat, summarize, or accidentally expose; a runtime-managed credential can be scoped, rotated, and revoked independently.
What an agent may store
| Data or copy | Why it exists | Questions to ask |
|---|---|---|
| Abuse-monitoring and security logs | Detect misuse, troubleshoot incidents, or enforce policy | Do logs include prompts, responses, tool results, or derived metadata? What is the default period and are exceptions available? |
| Conversation, thread, or session state | Continue work across turns or resume a task | Is state durable? Who can delete it? Does deletion remove attached files and events too? |
| Uploaded files and tool outputs | Support retrieval, code execution, search, or later steps | Where are they stored, and does deleting the conversation delete these copies? |
| Memory summaries and prior-run records | Personalize future runs or avoid repeating work | What is written, who can search it, how is it corrected, and can another agent read it? |
| Worker or sandbox files | Hold downloads, generated artifacts, caches, and intermediate results | Is the worker isolated between sessions? Who handles leftover files and backups? |
| Downstream application records | Publish a response, update a ticket, send a message, or write to a database | Which service becomes the data controller or processor, and what retention terms apply there? |
Retention depends on the exact product and feature
Retention figures are scoped examples, not a universal AI-agent rule. Documentation and contracts change, so verify the current terms for the deployment you use.
OpenAI services
OpenAI’s API data-controls documentation separates abuse-monitoring logs from feature application state. API data is not used to train or improve models unless a customer opts in. Abuse-monitoring logs may include prompts, responses, and derived metadata and are retained for up to 30 days by default. Eligible customers can request Modified Abuse Monitoring or Zero Data Retention (ZDR), subject to approval and endpoint limitations. Under ZDR, some endpoint store behavior is forced off. These API provisions should not be generalized to consumer ChatGPT or every OpenAI service.
The Agents API provides agents, optional environments, durable sessions, events, and items. Sessions retain state so work can continue across turns; sessions and published artifacts can be deleted. The documented data residency is currently US-only, and the Agents API does not support ZDR. Running a self-hosted sandbox does not by itself make the managed Agents API ZDR-eligible.
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The Agents SDK memory feature can inject a small summary at the start of a run and search or open relevant prior work progressively. A configured workspace may contain session files, memory summaries, and raw or consolidated memory. Live updates require filesystem capability, and separate memory layouts can isolate agents from one another.
For connected Google apps, OpenAI says access follows Google account permissions and workspace settings. If Memory is enabled, eligible information may inform personalization. Its connected-app FAQ says that connected-app data is not used to train generalized models except in described cases such as submitted feedback, manual copying or uploading, or content included in a response. That statement applies to the connected-app data covered by that FAQ, not automatically to every product or data type.
Anthropic deployments
Anthropic’s API retention documentation applies to the Claude API and named platform deployments where Anthropic is the processor. On Amazon Bedrock and Google Cloud’s Agent Platform, the cloud provider is the processor, so that provider’s documentation governs retention.
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Anthropic describes ZDR as an organization-level arrangement in which eligible prompts and responses are not stored at rest after the API response returns. Other features have different schedules. The current documentation describes six-year retention for Activity Feed data, six-year defaults for local Cowork and Claude Code transcripts unless an organization sets a finite custom period, and six-year retention for cloud remote-session transcripts unless deleted sooner. Claude.ai chat, file, and project content follows the organization’s retention setting unless deleted sooner, while Compliance API data has its own model. These periods apply only to the named categories.
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For self-hosted managed-agent sandboxes, Anthropic says content is outside its data-lifecycle controls after delivery to the worker. The customer must manage retention, redaction, deletion, filesystem permissions, isolation between sessions, and leftover local memory copies. Tools and MCP servers running in the sandbox have the worker’s permissions and can change the local copy.
Microsoft Foundry Agent Service
Microsoft identifies prompts and generated content, customer data uploaded for tools, external outputs from Bing grounding or function calling, stateful Threads, Messages and Runs, and retrieved grounding information as processed data. MCP header data is available only for the current run and does not persist. When an agent is published to Microsoft 365 or Teams, those services process and store associated data under their own applicable terms and commitments. Check the configured model endpoint and every connected tool rather than relying on the Agent Service label alone.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Memory and agent-to-agent sharing
Memory is a storage system with access-control and accuracy risks, not an invisible extension of the model. It can contain summaries, embeddings, conversation histories, learned patterns, intermediate reasoning states, or links to source records. A later run may retrieve stale or incorrectly scoped information, and a second agent may read or modify the same store.
AWS Prescriptive Guidance treats shared agent memory as partially trusted. It recommends least-privilege or read-only permissions, validation and grounding before memory is used, and a deterministic gateway that checks integrity, enforces policy, and creates audit trails. This is an architectural recommendation, not evidence that every agent stores reasoning chains or shared memory.
Quick Recap
How to compare two agents or architectures
| Comparison axis | Questions |
|---|---|
| Reach | Which apps, scopes, databases, files, web sources, and APIs are accessible? Are grants user-delegated, service-account-based, or administrator-managed? |
| Action rights | Can the agent only read, or can it write, delete, send, export, or publish? Are approvals required for consequential actions? |
| Execution boundary | Does it have a shell, filesystem, mounted data, browser, network, or MCP tools? Who isolates those capabilities? |
| Persistence | Which prompts, outputs, files, tool results, sessions, memories, and logs persist, and who can delete each copy? |
| Retention and training | What period applies to each feature? Are special retention arrangements available? Is content used for model improvement under the relevant terms? |
| Recipients and geography | Which model endpoint, connected service, cloud provider, publishing surface, or subprocessor receives data, and where is it processed? |
| Audit and control | Can administrators inspect actions and memory updates, revoke a tool, narrow an app scope, and review access records? |
Controls that reduce exposure
- Inventory every input route. List prompts, uploads, app connectors, APIs, browsers, search providers, mounted directories, shells, MCP servers, and publishing destinations.
- Apply least privilege. Prefer read-only scopes, narrow folders, separate service accounts, short-lived credentials, and role-based app access. Do not mount an entire drive when one directory is sufficient.
- Separate approval from execution. Require a human confirmation before sending messages, deleting records, publishing content, exporting data, or changing production systems.
- Keep secrets outside instructions. Inject credentials through the runtime or a secret manager; never paste API keys into prompts or memory.
- Set memory and retention rules. Decide whether memory is needed, what categories may be written, who can read them, how stale entries are corrected, and how deletion propagates to backups and worker copies.
- Audit downstream recipients. Record which tool received each payload and whether its provider, cloud region, or collaboration platform applies a separate lifecycle.
- Test isolation. Run separate sessions with test data and verify that files, memory, environment variables, and tool credentials cannot cross session or tenant boundaries.
- Review volatile terms. Recheck provider documentation and contracts before relying on a retention, residency, training, or ZDR statement; the figures above were documented as current on October 13, 2026.
Common misconceptions
- “Agents can see everything.” They cannot reach systems or records outside their configured tools and permissions, although a broad mount or powerful service account can make that boundary dangerously large.
- “Agents see only the prompt.” Retrieval, files, command output, browser pages, and external tools can add substantial content to the model context.
- “Not used for training means not stored.” Training, processing, security logging, feature state, and customer-side storage are separate questions.
- “Deleting the chat deletes every copy.” A worker, connector, published message, backup, or downstream system may have its own deletion process.
- “Memory is harmless personalization.” Memory can expose sensitive summaries to later runs or other agents unless its permissions, freshness, and deletion are managed.
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