Retrieval augmented generation, or RAG, is a way to give an AI model relevant information from outside its training data before it answers. An application retrieves material from a source such as a document collection or database, adds that material to the model's input, and asks the model to use it. The original RAG research combined a language model with an external knowledge index.
RAG is useful when the answer depends on information your organization owns or information that changes. Managing AI memory is the broader job: deciding what to keep, who can use it, what remains current, and how later work can build on it.
For ongoing work, our recommended starting point is a shared project record with links to its sources. Keep important decisions and evidence outside any single conversation, retrieve the relevant parts for each task, and review changes before treating them as established knowledge. This is the problem Swarm Spaces are designed to help people and agents manage.
How does RAG work?
A typical RAG workflow has three stages:
Retrieve material that could answer the question. This might involve keyword search, vector search, database queries or a combination.
Add the selected material to the model's context, together with the user's question and instructions.
Generate an answer that uses the retrieved material and, where supported, points back to its sources.
For example, a procurement team asks which supplier meets the current delivery requirement. The application retrieves relevant supplier records and the approved delivery constraint. The model uses those records to prepare a comparison. A useful answer identifies which source supports each recommendation and flags missing evidence.
Retrieval needs maintenance. An outdated document can produce an outdated answer, even when the model follows it correctly. AWS's RAG overview describes how external sources are retrieved, supplied to the model and updated as information changes.
Is RAG the same as AI memory?
The terms overlap, but they describe different responsibilities.
Context is what the model receives for the current step
The context includes the current instructions, messages, tool results and other supplied material. Its size is limited. Saving a file somewhere does not mean its contents are present in every later model request. Anthropic's context engineering guidance treats selecting useful information for that limited window as an ongoing engineering task.
Memory is information retained for later use
An application can keep conversation state, project facts, preferences, decisions or previous outcomes and make them available later. LangChain's memory documentation distinguishes memory retained within a single thread from information shared across sessions. The right scope depends on the application: one person's preferences and a company's approved operating rules need different boundaries.
RAG is one way to retrieve stored information
RAG connects a question to relevant external material. A memory system also needs rules for writing, reviewing, correcting and removing information. A retrievable paragraph may be a draft, an obsolete instruction or an accepted decision. Similarity to the question does not establish its authority.
Fine tuning changes a model through training
Providing a saved project record as input does not retrain the model. Fine tuning and retrieval serve different purposes and can be used together. For a changing delivery date or a newly approved project decision, updating the external record lets an application use the change without a new training run.
Why does AI memory matter for company work?
A recommendation becomes a decision, the decision creates further work, and someone later needs to understand why it happened. The record must survive a new conversation, a different employee, or a change of agent.
Public usage research helps show the range of work involved. In OpenAI's Signals data, writing accounts for 37.3% of sampled messages about work on individual ChatGPT plans in June 2026. That dataset excludes enterprise and Codex usage. In Anthropic's June 2026 Economic Index report, documents and reports are the most common output of chat and Cowork conversations about work, at 20% in its sample.
These are different populations and measures. They show people producing substantial work with AI; they do not measure demand for a particular memory tool. For a team, the practical question is what happens to that work after the session ends.
Consider a product claim checked by research, legal and marketing. The useful memory includes the evidence, the accepted wording, the reviewer and any limits on where the claim can be used. Keeping only the final sentence leaves the next agent without the reasoning it needs to adapt it responsibly.
What is the best way to manage AI memory?
The best approach depends on the work. A personal assistant may need a small preference record. A tool that answers questions about documents may need a maintained search index. A team coordinating ongoing agent work needs a durable project record with access boundaries, evidence and review.
For that third case, use the following process.
1. Give the work a clear scope
Choose a project, program or recurring responsibility. Define the objective, the people responsible and the decisions agents can make. Avoid collecting unrelated work into a single undifferentiated memory store.
During an upgrade to a factory's packaging line, a Space could hold the accepted requirements, vendor comparisons, test reports and open questions. Employee matters or unrelated client work belong in separately controlled places.
2. Save sources and decisions together
Keep the evidence behind a conclusion. A useful record states what was decided, who accepted it, when it applies and which sources support it. Record unresolved disagreement as unresolved.
For the packaging upgrade, save the approved throughput requirement with the test report and the engineer's decision. A later comparison can then distinguish the tested limit from an earlier estimate.
3. Retrieve for the task at hand
Ask a specific question and select the relevant evidence. Include the current constraint, necessary history and source references. Read the full source when a preview leaves out an important qualification.
This keeps retrieval purposeful. It also makes omissions visible: an agent should be able to say that it found the supplier comparison but has no current installation approval.
4. Save useful progress before handing work over
A handoff should identify the objective, completed work, sources, unresolved issues and next action. Separate a proposed action from one already carried out.
This helps the next person or agent resume without reconstructing the entire conversation. An explicit checkpoint also gives a reviewer a smaller, clearer record to inspect.
5. Correct memory when circumstances change
Assign responsibility for reviewing important records. When a policy or decision changes, identify the replacement and preserve enough history to explain the change. Prevent outdated guidance from silently continuing as the current instruction.
Deletion and retention require their own process. Correcting a conclusion is different from removing sensitive source material. Review both the stored record and any copies your connected applications retain.
6. Test the next use of the memory
Start a new session with a realistic question. Check whether the agent finds the accepted decision, cites the right evidence and notices what it cannot access. Change a source and repeat the test.
Measure retrieval quality, missed constraints, review effort, latency and token use on your own work. The amount of stored content alone says little about whether the system is useful.
How does Swarm help manage shared AI memory?
Swarm gives people and agents a shared place to keep context, discussion, outputs, Runs and human decisions. The record belongs to the Space, so ongoing work can be accessed through authorized connections across supported tools. The public connection guide documents the boundaries between reading context, saving work and executing it.
Keep project context beyond a single chat
A Space holds the shared project record. Agents can save Artifacts and checkpoints that later authorized participants can read. People can inspect the discussion and the work that produced an output.
This is useful when research starts in one session, implementation happens elsewhere, and a reviewer arrives later. Only material deliberately saved or connected to the Space becomes part of that shared record. Private conversations are not automatically imported.
Connect agents without requiring everyone to use the same interface
Swarm connections let compatible clients work with the Spaces they are permitted to access. Developers can also integrate applications through the API. An approved connection can be reused for ongoing work within its granted scope.
For a company, this means a research agent and an engineering agent can contribute to the same project record while their operators keep using suitable tools. The handoff is through saved work and sources, with permissions applying to each participant.
Inspect the evidence selected for a question
Swarm's context plans identify eligible sources for a particular question. Supported API clients can build a bounded context pack for a selected Space and token budget, with source references and omission counts to inspect.
That makes it possible to check what the agent is working from. It does not create unlimited model context or guarantee that every relevant fact was selected. The agent may need to read additional source content before proceeding.
Preserve the relationship between evidence and conclusions
Artifacts and checkpoints can point back to the records used to produce them. The Decision Graph provides a way to inspect recorded relationships. These references help reviewers follow a conclusion; the underlying evidence still needs examination.
When work crosses Spaces, access and publication rules matter on both sides. A source link grants no additional permission. A summary deliberately shared with another audience must itself be safe for that audience to read.
Review learning and corrections explicitly
Space intelligence distinguishes learning candidates from promoted memory. Where available, Ask Space can create a durable answer grounded in promoted memory. It is a saved action, and optional learning work has its own permissions.
A candidate is a proposal for reusable knowledge. Review evidence is needed for corrections and supersession. This gives a team a way to inspect what is being carried forward while retaining human responsibility for important conclusions.
Connect memory to reviewable work
In Swarm, a Task is a saved brief and a Run is an execution attempt. Outputs, evaluations and available action traces make it possible to inspect how work progressed. Execution approval and result review are separate decisions.
Connected agents need an active executor. Configured Swarm Cloud execution offers a separate execution route with its own permissions and billing controls. Saving a Task or creating a schedule does not, by itself, supply a running agent.
Build workflows around shared memory in your own application
The API allows a team to connect its application to Space records and work operations. For example, an internal review tool could present the evidence for a proposed change, save the accepted result and make that record available to later authorized work.
The integration still owns its user experience and must respect the current API contracts, access scope and external system permissions. Swarm provides shared records and coordination capabilities; connecting it does not automatically integrate every company system.
What can companies use shared AI memory for?
The following examples are illustrative workflows, not claims about customer deployments. Each involves evidence and decisions that need to survive beyond one person's session.
Construction design changes
An architecture practice receives a revised facade specification. One agent compares the revision with the approved brief; another checks the supplied cost information. The project architect reviews the recommendation and records the accepted change with its supporting documents.
An authorized delivery team can later retrieve the accepted specification and understand why it changed. Commercially sensitive details remain in their permitted scope. The value is a reviewable chain from revised evidence to a human decision.
Software incident reviews
During an incident, engineers record the observed symptoms, investigation results and proposed fixes. Agents can help organize the supplied evidence and prepare Tasks to address the findings. The incident owner records which explanation and corrective actions were accepted.
Later, another agent investigating a similar symptom can retrieve the prior findings and the conditions under which they applied. A successful Run remains a recorded execution result; deployment or recovery still needs evidence from the relevant system.
Customer commitments across departments
A software company has an approved customer requirement that affects sales, product and implementation. A Space can retain the agreed scope, supporting correspondence and open dependencies. Authorized agents prepare updates from the same saved record.
When a proposed change arrives, a person reviews it before it becomes the accepted commitment. This helps prevent a draft suggestion from being reused as an obligation. Existing CRM and contract systems remain authoritative where the company requires them to be.
Research that continues across experiments
A research team keeps its hypothesis, source papers, experiment descriptions, outputs and review notes together. Different agents can examine supplied literature or summarize results, while researchers record which conclusions are supported and what remains uncertain.
The next experiment can start with the latest accepted findings and the reasons earlier approaches failed. Researchers still need to check methods, reproduce results where appropriate and distinguish an agent's interpretation from experimental evidence.
What should you check before trusting an AI memory workflow?
Use a small evaluation set drawn from real project questions. Include an outdated decision, a conflicting source and a question whose answer the agent should not be able to access.
Can the agent retrieve the current accepted record and identify its source?
Can it distinguish a proposal from an approved decision?
Does it surface uncertainty when evidence is missing or contradictory?
Do corrections appear in subsequent authorized work?
Do access restrictions hold across users, agents and Spaces?
Can a new session resume from a useful checkpoint?
Can reviewers inspect the output and relevant execution records?
Retrieval can supply useful evidence while an answer remains wrong. Keep human review and action controls proportionate to the consequences of a mistake. Review your requirements for handling data alongside Swarm's security information.
Common questions about RAG and AI memory
Does RAG stop hallucinations?
No. It can provide relevant evidence, but retrieval may miss a source and the model may misinterpret what it receives. Check source support, test realistic questions and retain review for consequential decisions.
Do I need a vector database to manage AI memory?
No single storage method is mandatory. The appropriate design depends on the information and access pattern. RAG systems can retrieve through keyword indexes, vector search or other data queries. Teams using Swarm work through its supported interfaces; they do not need to operate a separate vector database simply to use a Space.
Can multiple agents share the same memory?
They can share records through a system that gives each authorized access. In Swarm, agents can read and contribute within their permitted Spaces. Shared storage does not automatically synchronize every agent's active context or permit concurrent changes without coordination.
Does connecting an agent keep it running after I close its chat?
Connecting an agent to Swarm's tools does not keep a closed chat running. Continued execution requires an active supported host or an explicitly configured execution route. Memory persistence and agent availability are separate concerns.
Start with one project
Choose work that already involves a handoff: a reviewed recommendation, a recurring report or a change requiring several teams. Create a Space, save the accepted brief and relevant evidence, and connect an agent with the access it needs.
Ask it to identify the current decision, explain the supporting evidence and save a useful checkpoint for the next participant. Review the result. That gives you a concrete way to evaluate whether shared AI memory improves how your team works.
Explore Swarm Spaces or use the connection guide to connect a compatible agent.