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A useful team-facing AI agent should not save every conversation. It should retain a small, revisable record of important decisions—the choice, why it was made, what alternatives were rejected, and where the supporting evidence lives—and retrieve that context later only when relevant and authorized. This guide lays out an architecture for building that system; it does not claim a working implementation or test results.
What “remembering a decision” should mean
Microsoft’s multi-agent architecture guidance puts the purpose plainly: “Memory is what turns a stateless request/response assistant into a system that accumulates context over time.” For a team agent, that accumulated context should be a curated set of durable facts, not a transcript archive. Microsoft’s memory architecture guidance distinguishes session memory, durable semantic and episodic memory, and procedural memory. It also distinguishes memory from knowledge sources such as document repositories and search indexes, which remain authoritative content and can change independently.
A decision can be worth preserving because its rationale and collaboration context may disappear into a long conversation. The policy, specification, ticket, or other source record should still live in its authoritative system. Store enough in memory to help the agent find and explain the decision; retrieve the current source when its exact wording or latest state matters.
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Keep three kinds of context separate
- Session history: the messages and context needed to continue the current interaction.
- Curated memory: selected durable facts, such as a decision and its rationale, that may matter in later conversations.
- Authoritative knowledge and records: documents, tickets, policies, and other records maintained in their own systems.
These have different purposes and lifecycles. Microsoft Learn notes that an interaction-scoped session can be resumed, while durable memory can be recalled in later, unrelated conversations; deleting a session does not delete memory kept in a separate store. The Azure Databricks documentation on agent memory and sessions makes that separation explicit.
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What to put in a decision record
Model a decision as a structured, revisable record—not just a sentence or an embedding. The following fields are a practical design recommendation, not a schema mandated by Microsoft’s documentation:
- Identity and scope: a decision ID, project or team, and access classification.
- Question and outcome: the problem being resolved and the selected option.
- Rationale: why the option was chosen, including constraints or goals that shaped the choice.
- Alternatives: options considered and why they were ruled out.
- Evidence: links to the relevant specification, ticket, discussion, or other authoritative source.
- People and timing: decision owner or participants and the date.
- Lifecycle: current status, any superseding decision, and a review date where appropriate.
The selected outcome alone is not enough to answer “Why did we choose this approach?” Rationale and rejected alternatives are what let an agent explain the choice rather than merely repeat it. Evidence links let it distinguish a remembered summary from the source record.
Choose what the agent is allowed to remember
Microsoft’s long-term memory guidance identifies decisions and commitments—including what was agreed, promised, or ruled out and why—as suitable durable content. It cautions against indiscriminate retention, sensitive information kept without intent, duplicated transactional records, and credentials or secrets. The long-term memory guidance supports an extraction policy that favors an explicit request to remember something or a repeated, durable signal over a single incidental remark.
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Before writing a candidate memory, check whether it belongs in durable memory at all, whether it contains sensitive material, and which scope may access it. Make the sharing boundary explicit: user, project, team, tenant, or organization. Do not infer that a fact is safe for every agent or team simply because it appeared in a conversation.
Design storage and retrieval around the decision
Do not choose a storage system based on vector search alone. The architecture guidance describes combinations such as relational or document profiles for semantic facts and vector indexes for episodic recall, while treating hybrid designs as use-case dependent. For decision memory, retain structured identifiers and metadata—such as project, date, status, and permission scope—so retrieval can filter candidates before using semantic similarity.
Similarity can surface a related decision that is obsolete or belongs to another team. A robust retrieval path should apply identity and permission checks, scope filters, and status checks; then use semantic matching to locate relevant records. When needed, follow the record’s evidence links to the authoritative source and verify its current content and permissions there.
The main design choices are:
- Scope: which users, projects, teams, or tenants share the memory.
- Source of truth: whether the memory is a curated index of context or a duplicate of an authoritative record.
- Storage and retrieval: structured records, semantic or vector retrieval, or a hybrid.
- Permission handling: how identity is enforced, how tenants or teams are isolated, and what happens to cross-team information.
- Lifecycle: who can inspect, correct, supersede, retain, or delete a memory.
- Operations: who reviews extraction quality, resolves errors, and maintains source connectors.
Microsoft’s memory architecture patterns describe design options, not a universal winner. The guidance does not establish that one storage approach is always faster, cheaper, or more accurate than another.
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A practical lifecycle separates extraction, approval, persistence, retrieval, and cleanup. Treat this as a recommended design sequence, not a built-in feature of a particular product.
- Collect session context. Use the active conversation as input, while keeping session history distinct from durable memory.
- Extract candidate decisions. Look for explicit decisions, commitments, rationale, and alternatives rather than summarizing every exchange.
- Check intent, scope, and sensitivity. Prefer explicit “remember this” requests or repeated durable signals; reject secrets and material that should not persist.
- Resolve conflicts. Compare the candidate with existing records. Correct an existing entry or mark a new one as superseding it rather than silently leaving contradictory active memories.
- Persist provenance and status. Store the source, date, owner or participants, access classification, and current status alongside the decision.
- Retrieve on demand. Search only when relevant to the user’s question, and enforce authorization before returning a result.
- Provide inspection and correction. Let authorized users see what the agent retained and fix inaccurate or outdated context.
- Apply retention and deletion rules. Define how records expire or are removed, and handle session deletion separately from durable-memory deletion.
Where meeting archives fit
Microsoft documents AI-generated archives for Teams meetings that condense discussion and metadata for downstream grounding without storing raw user content in the archive; administrators can disable archive generation. Microsoft’s AI Archives documentation describes this vendor-specific option. An archive may help locate meeting context, but it should not be treated as proof that a generated summary captured a decision’s rationale accurately. Keep a curated decision record and source links for decisions that need reliable future explanation.
Build it yourself or use a decision-context service?
A custom agent gives a team control over its schema, boundaries, retrieval, and lifecycle, but it also leaves that team responsible for extraction quality, permission enforcement, corrections, retention, and connectors. A dedicated service is another option when the goal is to connect decision context to engineering work.
Align describes itself as an “engineering decision graph.” Its documentation says it connects decision capture from Slack, Teams, Jira, Confluence, and meeting transcripts to a graph of decisions and evidence, then checks code changes against prior decisions. Align’s documentation presents this as a way to make decision context available to coding agents. These are vendor descriptions; they do not establish independent performance results or a controlled comparison with a custom build.
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