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An AI agent should store only selected, durable context that is specific to a user or ongoing task. Use retrieval-augmented generation (RAG) to fetch shared knowledge that can change, and tools to query live systems or take actions. Keep the current conversation’s working state in active context, and record consequential operations in an audit ledger when traceability matters. These roles complement one another; they are not competing all-or-nothing architectures.
What each kind of information store is for
Active context: what the agent needs right now
Active context is the conversation and working state needed to finish the current session or task. Keep the relevant state readily available, but do not automatically inject the entire conversation history into every prompt. A smaller, focused context can reduce unnecessary prompt content while preserving what the task requires. Google Cloud describes short-term conversational context as a distinct architectural need alongside long-term knowledge retrieval: Google Cloud’s agentic AI design patterns.
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Persistent memory: durable user- or task-specific context
Persistent memory is a curated set of information that should influence future interactions: for example, a user’s preferences, previous decisions, ongoing goals, or reusable patterns from past interactions. AWS guidance also identifies updated goals and success or failure signals as possible memory candidates. Memory is useful when the information is likely to change a future response or action and has enough context to be interpreted correctly. See AWS’s overview of agentic AI memory and its guidance on working memory.
RAG: external knowledge that must remain authoritative
RAG retrieves relevant material from an external knowledge source when the agent needs it. It suits documentation, specifications, policies, and other knowledge that exists independently of a particular user interaction, is shared across users or workflows, or may be updated separately from the agent. Microsoft’s reference architecture describes these sources as authoritative, shared, permission-controlled, and independently changeable. Retrieving the current source is safer than relying on a stale memory copy: Microsoft’s AI agent design patterns.
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Tools: live queries, operations, and actions
A tool is a callable interface to a search system, API, code runtime, or other operation. Use one when the agent needs to check current data or do something in an external system. Retrieval can itself be invoked as a tool when needed. Microsoft recommends clear tool descriptions and, where operational traceability matters, recording calls, parameters, and results. See Microsoft’s AI agent design patterns.
Audit records: proof of what happened
An audit or transaction record is a durable ledger for actions that need operational or compliance traceability. It is not a substitute for memory or RAG: chat history may not provide an adequate record of consequential operations. Google Cloud treats auditability as a distinct concern alongside conversational context and long-term retrieval: Google Cloud’s agentic AI design patterns.
Decide where each piece of information belongs
Classify information by ownership, change rate, access needs, and purpose rather than by whether it happens to appear in a conversation. The same system can use all these mechanisms, with different boundaries for each.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minute- Identify whose information it is. A user’s preference or an agent’s ongoing task state may belong in appropriately scoped memory. Shared organizational knowledge belongs in a governed knowledge source. Microsoft makes the division explicit: “If the workflow already exists as documentation, a runbook, or code, it belongs in a knowledge source or in a tool, not in memory.” This is an institutional statement in Microsoft’s multi-agent reference architecture, not a named individual’s quotation: Microsoft’s multi-agent reference architecture.
- Check how often it changes. Retrieve frequently changing facts from their current source; a stored copy may become stale. Durable preferences and decisions are more plausible memory candidates. See Microsoft’s AI agent design patterns.
- Choose how the agent should access it. Use active context for small, latency-sensitive state in the current task, RAG for relevant material in a larger knowledge store, and a callable tool for live queries or actions. AWS cautions against indiscriminately injecting full histories or treating one store as suitable for every access pattern: AWS’s working-memory guidance.
- Set read and update boundaries. Decide which users, projects, agents, or tenants may access or change a memory or knowledge source. Sharing boundaries are an explicit design choice in Microsoft’s architecture: Microsoft’s multi-agent reference architecture.
- Define ownership and lifecycle. Give persistent memory a scope and a reason to remain. Retire information that is stale, unused, or no longer permitted, and watch for retrieval precision to degrade as a store grows. These are implementation choices that follow from the architecture’s lifecycle and scope requirements; they are not universal retention intervals. See Microsoft’s multi-agent reference architecture.
- Decide whether an audit trail is needed. Record consequential tool calls or transactions in a durable ledger rather than assuming chat history is sufficient. See Google Cloud’s agentic AI design patterns and Microsoft’s AI agent design patterns.
The trade-offs include durability, change rate, source authority, retrieval latency, token cost, precision, access control, sharing scope, and auditability. Microsoft discusses trade-offs among injecting context into prompts, retrieving conversation history, and searching memory on demand; the right approach depends on the workload rather than a universal rule: Microsoft’s multi-agent reference architecture.
Examples: classify common agent information
| Information | Best fit | Reason |
|---|---|---|
| “The user prefers concise answers.” | User-scoped persistent memory | It is a durable preference that may improve future interactions. Retain it only under applicable consent, privacy, and retention rules. See AWS’s memory overview and working-memory guidance. |
| The current refund policy | Maintained policy source, retrieved with RAG | It is shared knowledge that may change, so retrieve the current authoritative version rather than relying on a memory copy. See Microsoft’s AI agent design patterns. |
| A live account balance | Tool or API query | The value must be fetched from the live account system. Treat the returned value as transient task context; record the access or operation in an audit trail if required. See Google Cloud’s agentic AI design patterns and Microsoft’s AI agent design patterns. |
| Progress halfway through a multi-step request | Active task state; persistent memory if the workflow must resume later | Keep it in the current working context while the task is active. If it must survive a session boundary, deliberately define its scope and expiry. This application follows the distinction between short-term context and longer-term memory in Microsoft’s multi-agent reference architecture, Google Cloud’s design patterns, and AWS’s memory overview. |
| A plan the agent tried that failed | Task-specific persistent memory when it can inform future work | Store the failure with the goal and context needed to interpret it; an unscoped failure note may mislead a later task. AWS identifies success and failure signals as possible memory material: AWS’s working-memory guidance. |
| A workflow already documented in a runbook or code | Knowledge source or tool | Keep the workflow in its maintained documentation or expose it through an appropriate tool rather than duplicating it in conversational memory. See Microsoft’s multi-agent reference architecture. |
Design memory so it stays useful and safe
Do not treat every conversation detail as a candidate for permanent storage. For each proposed memory, check that it is likely to change future behavior, has enough context to avoid misinterpretation, and is appropriate to retain under the system’s privacy and access rules. Preferences, prior decisions, ongoing goals, reusable interaction-derived knowledge, and success or failure signals are possible candidates in the cited AWS guidance: AWS’s memory overview and working-memory guidance.
As an implementation recommendation, attach provenance, scope, and lifecycle information to stored items. Provenance helps establish where a claim came from; scope limits retrieval to the right user or task; lifecycle rules make correction and retirement possible. These metadata choices implement the sources’ broader requirements for scope and lifecycle rather than a mandated universal schema. Microsoft identifies memory boundaries as a design decision, and warns that retrieval precision can degrade as stores grow: Microsoft’s multi-agent reference architecture.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common design mistakes to avoid
- Saving shared facts in personal memory: a policy or procedure can change and may need to be consistent across users. Keep it in a governed source and retrieve it when needed.
- Saving everything: indiscriminate history injection raises prompt cost and can make relevant details harder to use. Preserve a compact active state and retrieve historical material selectively.
- Using memory for live facts: a remembered value is not a substitute for querying the system that owns current account, inventory, or other changing data.
- Using chat history as an audit ledger: record important actions and transactions in a dedicated durable record when operational or compliance traceability is required.
- Sharing memory without boundaries: explicitly scope reads and updates across people, projects, agents, or tenants.
Microsoft’s architecture gives “2 to 3 seconds” as an example of a standard RAG request, but that is a vendor example, not a general latency guarantee or benchmark for every system: Microsoft’s AI agent design patterns. There is no universal performance figure that determines whether memory, RAG, or a tool is best. Validate a design against its workload’s retrieval quality, update behavior, latency, security boundaries, and evaluation results.
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