Orchestration Agent Long-Term Memory for Cross-Session Context

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Solution Overview

Problem

Existing neural network models, such as transformer-based models, require substantial computing resources for training and execution, and lack the ability to retain context across sessions, limiting the effectiveness of generative AI applications.

Innovation Solution

Implementing long-term memory for orchestration agent sessions that retain context across sessions, allowing developers to configure memory retention and enable/disable capabilities, and securely manage user-specific memory spaces.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If long-term memory is implemented for orchestration agent sessions, then context retention across sessions is improved, but device complexity increases

Engineering Contradiction:

Improvecontext retentionVSAvoidmemory management system

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments memory into distinct types (episodic, procedural, semantic, vector conversation index) organized in a hierarchical structure. Each memory type is managed separately with specific data structures and retrieval mechanisms, allowing complex memory functionality to be built from manageable modular components rather than a monolithic system.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces memory management components as intermediaries between the orchestration agent and the generative machine learning model. These intermediaries handle memory operations (storage, retrieval, updating) and present a simplified interface to the agent, isolating complexity in dedicated memory management modules rather than throughout the entire system.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If session context is retained across multiple sessions, then productivity is improved, but loss of time for managing memory increases

Engineering Contradiction:

Improveworkflow orchestrationVSAvoidmemory management time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent implements preliminary action by automatically generating summaries of session interactions and pre-organizing them into appropriate memory types during and immediately after sessions. This pre-processing of information into structured formats (episodic summaries, procedural steps, semantic concepts, vector indexes) eliminates the need for time-consuming manual memory management during subsequent sessions.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The memory system operates autonomously to manage context retention. It automatically captures session data, categorizes information into appropriate memory types, stores it in the data store, and retrieves relevant context when needed without requiring user intervention or manual management, thereby maintaining productivity while minimizing time investment.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20260017487A1Generating long-term memory for orchestration agent sessions

Publication Date: 2026.01.15 AMAZON TECH INC

AI summary

Long-term memory data objects may be generated for orchestrations agents. When a session completes or ends, a long-term memory data object may be generated according to a specified long-term memory type based on turn inputs during the session. When a new session is started, the long-term memory data object may be used as part of inputs to a generative machine learning model to perform or respond to turn inputs of the new session.