Turning Chat Logs into Knowledge Assets: AMS Agent Memory’s Long‑Term Memory

Introduction

Users of AI assistants often lose context when a conversation ends, forcing them to repeat preferences, project background, or usage habits in a new session. This repetition is inefficient for personal users and especially problematic in enterprise scenarios such as customer service, operations troubleshooting, and sales follow‑up, where interactions span multiple sessions.

The root cause is not model intelligence but the fact that most dialogues are discarded the moment they finish. A long‑term memory mechanism is needed to preserve useful information across sessions and tasks, rather than merely expanding the short‑term context window.

Concept of Long‑Term Memory

Short‑Term vs. Long‑Term Memory

Short‑Term Memory : a temporary window that retains raw events only for the current interaction.

Long‑Term Memory : enables cross‑session and cross‑task reuse by extracting dialogues, events, and operation results into structured, searchable memory entries.

Memory Strategies

AMS provides 13 built‑in long‑term memory strategies, each targeting a specific type of information and use case. The default General Memory strategy is always enabled and cannot be turned off.

General Memory – applicable to all scenarios; continuously stores basic context.

Summary Memory – aggregates interactions to retain key insights and maintain context continuity in multi‑turn dialogs.

Semantic Memory – extracts factual, objective knowledge for later retrieval.

Experience Memory – captures lessons learned and best practices from past interactions.

Scenario Memory – records personal experiences and events along a timeline.

User Profile – extracts stable identity, role, and background attributes.

User Preference – derives behavior patterns and habits from raw dialogue.

Entity Knowledge – extracts concrete entities (people, systems, project names) and their factual attributes.

Pattern Knowledge – abstracts recurring relationships, high‑frequency behaviors, and rules for predictive services.

Event Memory – records objective events with clear timestamps for audit and temporal analysis.

Case Memory – preserves complete incident cases, including cause, analysis, handling process, and outcome.

Tool Knowledge – stores tool usage, parameters, and applicable scenarios to support automatic tool recommendation and invocation.

Skill Flow – codifies repeatable SOPs or methodologies for workflow automation and knowledge transfer.

All strategies except General Memory can be individually enabled, disabled, and assigned an expiration time (minimum 7 days). Short‑term memory can be set to “Permanent” or a custom number of days.

Ownership and Isolation

Long‑term memory is automatically scoped by user, agent, session, and application, ensuring that memories do not interfere across different entities. The isolation is transparent to users but guarantees that:

Memories of different users or applications remain independent.

The same user across different agents or apps has separate memory contexts.

Retrieval, cleanup, and archiving operate precisely within the defined scope.

Lifecycle Management

Each strategy can define its own retention period; memory older than the configured days is automatically cleared. The system enforces a minimum retention of 7 days for long‑term strategies. Short‑term memory may be permanent or have a custom expiration.

Disabling a strategy stops the generation of new memory entries, but existing entries persist until their TTL expires, becoming “orphan memory” that cannot be read or written by new dialogs.

Effect of Strategy Changes

Modifying a strategy does not rewrite historical data; changes only affect future conversations. Deleting or disabling a strategy leaves existing memories untouched, but they become inaccessible to new dialogs. Built‑in strategies, once enabled, allow only expiration time edits; custom strategies allow name, expiration, and prompt edits after activation.

Typical Scenarios Solved by Long‑Term Memory

Personalization & Consistent Tone : Store user preferences (e.g., “answer concisely”) via the User Preference strategy and identity details via User Profile to maintain style across sessions.

Cross‑Session Knowledge Reuse : Use Semantic Memory for facts, rules, or constraints; use Entity Knowledge for specific entities and their attributes.

Task Progression & Recall : Apply Summary Memory to capture stage‑wise conclusions; combine with Scenario or Event Memory for timeline‑based backtracking.

Experience Reuse & Issue Resolution : Leverage Experience Memory, Case Memory, and Skill Flow to preserve troubleshooting lessons, complete case histories, and SOPs.

Tool Invocation & Pattern Insight : Tool Knowledge records tool usage details; Pattern Knowledge abstracts recurring patterns for proactive recommendations.

Multi‑Dimensional Cooperation : In complex business contexts, multiple strategies work together—General Memory for baseline context, Summary Memory for stage summaries, Semantic & Entity Knowledge for stable facts, User Profile & Preference for identity, and Case & Skill Flow for experience reuse.

AMS Long‑Term Memory Capabilities

Memory Strategy Management

Strategies define what to retain, how long, and under which scenarios. They guide extraction, storage, retrieval, and expiration, shaping the final form of memory entries.

Message Extraction Scope & Custom Strategies

Both built‑in and custom strategies allow configuring which message roles participate in extraction, acting as the first filter to reduce noise.

Memory Extraction & Ingestion

Raw dialogues, event streams, task results, or tool outputs are first filtered by the extraction scope, then, when trigger conditions are met, refined into summaries, facts, experiences, or preferences according to the selected strategy. The resulting entries are stored with metadata such as strategy ID, type, timestamps, and ownership dimensions.

Retrieval & Recall

Effective value lies in accurate recall. Common recall methods include:

Auto‑injecting user preferences at the start of a new session.

Retrieving historical facts or rules when similar questions arise.

Recalling previous summaries and pending items during task continuation.

Semantic relevance‑based retrieval before answering or reasoning.

If memory cannot be retrieved, it remains a static repository with no practical benefit.

Ownership Management (Subject Management)

AMS automatically groups memories by user, agent, session, and application, presenting them in a unified “Subject Management” view for inspection, troubleshooting, and audit.

Access Credentials & Governance

API keys serve as the unified authentication mechanism. Keys should be created per environment and purpose, with proper masking and revocation to secure memory access.

Memory is not meant to be permanent; expiration policies and TTL‑based cleanup control retention. Governance includes manual inspection, filtering, cleanup, and using request logs to locate anomalies.

Configuration & Validation

Entering the Configuration Page

After logging into the AMS console, select “Memory Strategies” from the left navigation to view and edit current configurations. Switch to the target workspace if needed.

Configuring Short‑Term Memory

Choose between “Permanent” (no expiration) or “Custom” (specify days) for short‑term memory retention.

Enabling Long‑Term Memory Strategies

In the “Long‑Term Memory” section, enable or disable the 13 built‑in strategies (General Memory must stay on). Edit each strategy to set its expiration (minimum 7 days) and define the message extraction scope.

Creating Custom Strategies (Optional)

Click “Add Custom Strategy” to open a new strategy card.

Enter a name (max 48 characters) and select a strategy type; default prompts are auto‑filled.

Set expiration (minimum 7 days) and message extraction scope.

Configure trigger conditions such as idle timeout, max token size, new event batch, or historical events.

Edit the prompt template as needed.

Test the strategy with sample data; after a successful test, close the dialog.

Save and enable the strategy.

Before enabling, all fields except the strategy ID can be edited; after enabling, only name, expiration, and prompt can be changed. Deletion requires the strategy to be disabled first.

Validating Effectiveness

Verify the full pipeline – generate, write, retrieve, and recall – primarily via the “Subject Management” view:

Check the “Memory Details” tab for generated entries and their metadata (strategy_id, type).

Confirm that retrieval hits the expected content.

Inspect the “Request Records” tab for SEARCH/ADD events, ensuring no failures.

Assess whether recalled results actually aid the current dialog or task.

Successful write‑only does not guarantee value; accurate recall and impact are essential.

Best‑Practice Recommendations

Define what to remember – facts, preferences, summaries, experiences, or full cases – and select corresponding strategies.

Start with a few high‑value strategies; avoid enabling all 13 at once to reduce noise.

Confirm immutable configurations (extraction scope, strategy type) before enabling, as built‑in strategies only allow expiration edits later.

Set clear expiration times (minimum 7 days) to prevent uncontrolled accumulation.

Test custom strategies thoroughly before production use.

Use Subject Management for routine troubleshooting – filter by type, time range, and request logs.

Manage API keys per environment and purpose; revoke unused keys promptly.

Periodically review memory quality – noise ratio, expiration effectiveness, and strategy fit – and adjust prompts or extraction scopes as needed.

Conclusion

The true value of long‑term memory lies not in the sheer volume stored but in the ability to retrieve truly valuable information at the right moment and integrate it into ongoing context. Only when strategy definition, extraction, retrieval, recall, and governance are all properly implemented does long‑term memory become a stable, reusable, and manageable knowledge asset.