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First, separate memory from the context an agent sees
A memory layer is not a giant prompt that every agent receives. It is a way to retain and retrieve selected context; the working memory assembled for a particular model call is what that call sees. Microsoft’s Multi-agent Reference Architecture puts it succinctly: “Working memory is the only thing the model ever sees.”
- Short-term memory holds recent session context, such as decisions and unresolved questions in the current task.
- Long-term memory retains selected information across sessions, such as reusable preferences or task specifications.
- Working memory is the relevant material assembled for one model call. It may draw on session history, durable memory, current instructions, and retrieved source material.
These are different jobs, not necessarily three separate databases. The important point is to choose what to retain and what to load for each call instead of treating a full transcript as the default memory.
Choose how context crosses agent boundaries
The pattern determines who can see or change context, where the canonical state lives, and how much information travels with each message. Microsoft’s architecture guidance describes shared, distributed, and hybrid short-term-memory patterns; Microsoft ISE separately compares approaches to passing context between agents.
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| Pattern | How it works | Advantages | Costs and risks | Good fit when |
|---|---|---|---|---|
| Shared store or context ID | The coordinator sends an identifier; permitted agents read or write the corresponding shared memory. | Small messages, a common source of truth, and useful support for long histories or centralized queries. | Agents need infrastructure access and credentials; storage calls add dependencies, and shared access expands the exposure surface. | Agents are trusted internal services, histories are long, or centralized querying matters. |
| Coordinator-embedded context | The coordinator retrieves and selects context, then includes it in each agent’s request. | The coordinator controls disclosure; agents can remain stateless and do not need memory-store access. | Repeated transfer increases payload; summaries can omit details that an agent needs. | Agents are independently deployed, cross organizational boundaries, or require tightly controlled disclosure. |
| Per-agent state | Each agent owns its state; a session or context identifier can correlate related work. | Supports agent autonomy and data isolation, including independent retention choices. | State can diverge; synchronization, migration, and audit aggregation take more work. | Agents need independent, long-running context and do not need one shared view. |
| Hybrid or subgroup memory | A selected group shares a memory area while other agents use separate or no access. | Limits visibility to collaborators and can partition task state or retention. | Membership and lifecycle must be managed carefully. | Some agents collaborate on a task whose state should remain unavailable to the rest of the team. |
There is no universally established best storage engine or coordination pattern. Microsoft’s short-term-memory guidance notes that document-oriented NoSQL systems can suit flexible, nested session data; that is architectural guidance, not proof that one database type is best for every workload. Choose based on access patterns, scale, deployment topology, sensitivity, consistency requirements, and operating constraints.
Design a 13-agent context flow
For a large team, make the information path explicit rather than assuming all agents share one conversation. A coordinator can route a task to specialists, retrieve the relevant memory, and assemble a narrowly scoped request for each one. The example below is illustrative: the number of agents does not itself dictate a specific topology.
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- Start with a task and scope. Create a stable session or context identifier and record the task’s purpose, owner, and applicable access boundary.
- Retrieve only relevant state. The coordinator reads allowed session or subgroup memory and retrieves any needed authoritative material from its source.
- Prepare an agent-specific request. Include the task, required inputs, output format, and only the context needed by that specialist. A 13-agent team need not send every specialist the same payload.
- Validate and record results. Check typed outputs against their expected shape. Save decisions or reusable results to the appropriate scope, with ownership and update information.
- Reconcile before acting. The coordinator compares outputs that affect the same decision, resolves or escalates conflicts, and updates canonical state rather than letting contradictory answers silently accumulate.
Passing context between agents is an architectural choice with consequences for the wider system, as Microsoft ISE notes. A coordinator is a useful policy point when the topology supports one: it can select what each specialist sees, but that also concentrates responsibility and can make messages larger.
Set memory scope, ownership, and access rules
“Shared” should describe a defined permission boundary, not mean that all 13 agents can read and write everything. Specify who owns canonical state and who may access each scope. A practical scope might be user, session, agent, subgroup, or tenant, depending on the system’s needs.
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- Assign an owner. Identify the service or role responsible for canonical updates, including who resolves conflicts.
- Separate read from write access. An agent that needs context for analysis may not need authority to alter the shared record.
- Use least privilege. Grant each agent only the access required for its task, and avoid distributing broad store credentials where a narrower interface will work.
- Define lifecycle behavior. Make expiry, retention, and deletion behavior explicit for each scope.
- Validate payloads and interactions. Use typed payload validation and audit cross-agent interactions, particularly when agents can write state that other agents rely on.
Microsoft Learn’s multi-agent guidance recommends limiting inter-agent context to what is necessary, validating typed payloads, applying least privilege, and reconciling conflicting outputs. These controls matter whether context travels in messages or through a shared store.
Remember useful context, not every transcript
Persist information because it is likely to be useful again, not merely because it appeared in a conversation. Microsoft’s reference architecture recommends weighting and contextual retrieval. Apple Machine Learning Research’s September 2026 publication describes retaining reusable task specifications, schemas, tool configurations, and output constraints while discarding session-specific reasoning traces.
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Useful candidates can include a confirmed decision, an unresolved issue, a durable preference, or a reusable task specification. Before storing a candidate, determine its owner, scope, expected lifetime, and whether it is safe and useful for another agent to see. Keep raw history only when a defined purpose and retention policy justify it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep authoritative documents outside conversational memory
A memory layer and a knowledge base are not interchangeable. Memory holds selected user, session, and collaboration context that might otherwise be lost. Business documents, policies, and other authoritative content belong in permission-controlled repositories or indexes because they can change independently.
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Retrieve those materials when needed and check authorization at query time instead of copying them indefinitely into agent memory. This keeps the source of truth in the system responsible for its access controls and freshness; memory can retain a useful decision or pointer without becoming a shadow document repository.
Compare the trade-offs before committing
Evaluate candidate designs against the same operational questions. A compact shared-store design may reduce payload size but still have significant permission and infrastructure costs; message-based context may simplify agent access while increasing transfer and coordination work.
- Privacy and access boundary: Which agents can read or write each memory scope?
- Canonical ownership: Where is the authoritative collaboration state, and who may update it?
- Consistency and audit: How are concurrent edits, conflicting outputs, and cross-agent actions detected and resolved?
- Payload and latency: Does the system pay for repeated context transfer, storage calls, or both?
- Autonomy and operations: How much state can agents manage independently, and what synchronization, migration, and monitoring burden follows?
Draw the flow from coordinator to memory and source systems to each agent. For every arrow, label the identifier or payload, the read/write permission, and the responsible owner. If the diagram cannot show which agent is allowed to receive a particular item, the sharing policy is not yet precise enough.
Read benchmark results in their evaluation context
Published results can inform a design discussion, but they do not establish that a particular pattern will win in another deployment. The figures below are vendor-reported evaluations, not universal head-to-head guarantees.
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| Publisher and evaluation | Reported result | What the figure applies to |
|---|---|---|
| Microsoft Research, AIM on MUMBench, 2026 | 96.0% visibility classification accuracy; 58.8% strict operation accuracy; 70.5% state-aware operation accuracy | The AIM page reports three independent runs on MUMBench. |
| Apple Machine Learning Research, 2026 | 96% task completion with shared selective persistent memory, versus 79% without memory and 71% with full-history persistence | Three enterprise deployment scenarios reported by Apple. |
| Apple Machine Learning Research, 2026 | 14× task-time reduction; 97× lower per-invocation token cost; success in 12/12 trials | Apple’s stated data-refresh and summary-driven generation experiments; the 12 trials covered four public datasets. |
These results concern the named evaluations and scenarios. They should not be read as a promise of the same accuracy, completion rate, time reduction, or token cost for a different agent team or workload.
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