Vertex Macro｜Financial Cloud Cloud · AWS Amarathon 2025
Vertex Macro｜Amarathon 2025 Recap 06：From Matching to Understanding: Personalized AI Search Practice Driven by AgentCore Memory
Search
● Search Engine Architecture Flow
Input
● Query
Retrieval Methods (from Query)
● Inverted index based lexical matching
● Item-based collaborative filtering
● Embedding-based retrieval
Merge Stage: Combines outputs from the three retrieval methods into
● Unordered product set without duplication
Ranking Stages
● Pre-ranking
● Relevance ranking
● Ranking
Additional Inputs to Mix-Ranking
● Advertising
● Content media
Final Ranking Stage
● Mix-ranking
System architecture for augmenting product matching using semantic matching
Input
● Query
Matching Components
● Behavioral Data
● Keywords Matching
● Semantic Matching
Data Flow
● Query feeds into Keywords Matching and Semantic Matching
● Behavioral Data provides a behavioral signal to Ranking
● Keywords Matching and Behavioral Data form an unordered match set
● Semantic Matching provides a semantic similarity score to Ranking
Processing Stage
● Ranking
Output
● Ordered product list
LLM Agent Workflow
Process Flow
● Query -->LLM -->Plan
● Plan -->LLM -->Actions
● Actions -->"Is the plan complete?" (Decision Point)
● If "yes" -->LLM -->Finalize answer
● If "no" -->Loop back to "Actions" (via an LLM step)
Decision Logic
● Uses an LLM to determine if the plan is complete.
● Differences
01 Traditional Search
● Relies on keyword matching
● Webpage link weight ranking
● Goal is to quickly retrieve massive information
02 AI Search
● Semantic understanding
● Related information injected into vectors + keywords
● Large models assemble information
● Goal is to accurately answer user questions
Memory
● LLM with Context Storage Architecture
Main Components
● User
● Chat APP
● LLM (Stateless)
● Context Storage
Workflow
● Send message (from User to Chat APP)
● Carry complete context (from Chat APP to LLM)
● Return response (from LLM to Chat APP)
● Store history (from Chat APP to Context Storage)
● Return result (from Chat APP to User)
Context Storage Content
● System Prompt
● Round 1: User Question
● Round 1: AI Answer
● Round 2: User Question
● Round 2: AI Answer
Architecture for an agentic system
● uses LLMs to manage memories and knowledge graphs from conversations:
Data Flow & Extraction
● Input: Conversations (messages) are fed into the system.
● Extraction LLM: A Large Language Model processes the messages to generate structured data.
● Outputs: "New memories" and "new entities & relations".
Storage Components
● Vector Database: Stores "existing + new memories" for retrieval and updates.
● Graph Database: Stores "existing + new entities & relations" for updates.
Update & Management
● Update LLM: A second LLM manages the data updates back into the databases.
● Functions: Manages "store updates" for both databases.
● Operations: Includes explicit "Add", "Delete", and "Update" actions for the stored data.
Overall Goal
● Memories ADD: The entire system facilitates the persistent storage and management of conversation context and extracted knowledge.
Enhanced Context Storage
System Components & Definitions
● System Prompt: System instructions or configuration.
● Dialog History: Record of the conversation.
● Tool Definitions: Available functions for the AI agent.
● weather_api(): requires location, date
● calculator(): requires expression
● Thinking: The agent's reasoning process.
Memory Interaction
● Extraction (from Thinking to Memory)
● Backfill (from Memory to Thinking)
● Memory: Long-term or short-term knowledge base.
Example Workflow: "Query weather (Fahrenheit)"
● User: Query weather (Fahrenheit)
● AI: Need to call weather API
● Tool Usage History:
● Call: weather_api(location='Beijing', date='today')
● Return: {'temperature': 25, 'condition': 'sunny'}
● Call: calculator(expression='25*1.8+32')
● Return: 77
Benefit
● Long-term retention and efficient management
● Continuous knowledge update
● Personalized service
● Complex task support
● Improve interaction quality
● From stateless to stateful
AgentCore Memory
Amazon Bedrock AgentCore
● Utilize any framework and model without managing infrastructure, safely build, deploy, and operate high-performance Agents at scale.
Components
● Runtime
● Memory
● Identity
● Gateway
● Code Interpreter
● Browser Tool
● Observability
Simplified Memory System Management
● Abstract memory infrastructure
● Based on serverless architecture for automatic scaling
● Automatically store and manage the context information of Agents across multiple sessions
Enterprise-Level Services
● Provide dedicated storage for each customer to fully guarantee data privacy
● Provide encryption protection and regionalized data storage to meet enterprise-level security needs
Deep Customization
● Customize memory modes according to specific application scenarios
● Set long-term memory extraction rules
● Select the appropriate model and customize the prompt words to optimize the long-term memory extraction effect
Agent Memory Components
Agent Core
● Agent Reasoning
● Agent State
● Knowledge Components
● Tool Calling
● Policy Definition
Short-Term Memory
● Context Window
● Conversation History
● Tool Calling History
Long-Term Memory
● Events Summary
● User Profile Information
● Document Information
Connections
● Storage - between Agent Core and Memory components
● Retrieve - between Memory components and Automatic Memory Retrieval Module
● Automatic Memory Retrieval Module
Agent Architecture & Benefits with Amazon Bedrock AgentCore Memory
Agent Functionality
● The intelligent agent automatically breaks down complex user needs (e.g., "Recommend a laptop under 5000 yuan for a university student that runs design software") into multiple parallel sub-tasks.
● Examples of sub-tasks: "University student usage scenario analysis", "Budget filtering", "Software running requirement matching".
Benefits of AgentCore Memory
● Relies on the technical advantages of AgentCore Memory's long-term memory.
● Significantly speeds up the Agent development process.
● Achieves multiple real-world application values:
● Token usage for smart searches based on user profiles is greatly reduced.
● Search result accuracy rate is effectively improved.
● Provides a massive breakthrough in technical competitiveness and cost-effectiveness.
System Diagram Flow
● User Query leads to Agent Runtime Environment (Strands Agents).
Strands Agents interacts with
● Tools (User Preference Retrieval)
● Amazon Bedrock LLM (Processes Output)
● AgentCore Memory
AgentCore Memory manages
● Short Term Memory (Interaction Events)
● Automatic Memory Extraction
● Long Term Memory (User Preferences)
● The process results in an Agent Response to the user.