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architecture-knowledge

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Mis à jour14 avril 2026 à 10:34

Deep knowledge about AI agent architectures, AWS services, memory patterns, and best practices. Use as reference when answering technical questions.

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Source

aws-samples

aws-samples/sample-agent-greenhouse

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Développeurs de logicielsProfessions informatiques et mathématiques·SOC 15-1252

SKILL.md

readonly

name	architecture-knowledge
description	Deep knowledge about AI agent architectures, AWS services, memory patterns, and best practices. Use as reference when answering technical questions.

Architecture Knowledge

AgentCore Runtime

Managed container runtime for AI agents
Supports Strands SDK, LangChain, and custom frameworks
Request-driven model (invoke_agent_runtime API)
Session-based with automatic scaling (including scale-to-zero)
Memory integration (STM events + LTM strategy extraction)

Memory Architecture (Plato Design)

Platform files (SOUL.md, IDENTITY.md) → baked into container image
User memory → AgentCore Memory with namespace isolation
STM: Conversation events per session (list_events / create_event)
LTM: Extracted records from 4 strategies (Semantic, UserPreference, Summary, Episodic)
Namespace isolation: /actors/{actorId}/ for server-side filtering
Explicit memory: save_memory / recall_memory tools for high-value facts

Slack Integration Pattern

API Gateway → Lambda (3s ack) → SQS → Worker Lambda (15min) → AgentCore
Session ID: thread-based for shared context, channel+user for non-thread
Actor ID: Slack user ID for memory isolation

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name	architecture-knowledge
description	Deep knowledge about AI agent architectures, AWS services, memory patterns, and best practices. Use as reference when answering technical questions.

Architecture Knowledge

AgentCore Runtime

Managed container runtime for AI agents
Supports Strands SDK, LangChain, and custom frameworks
Request-driven model (invoke_agent_runtime API)
Session-based with automatic scaling (including scale-to-zero)
Memory integration (STM events + LTM strategy extraction)

Memory Architecture (Plato Design)

Platform files (SOUL.md, IDENTITY.md) → baked into container image
User memory → AgentCore Memory with namespace isolation
STM: Conversation events per session (list_events / create_event)
LTM: Extracted records from 4 strategies (Semantic, UserPreference, Summary, Episodic)
Namespace isolation: /actors/{actorId}/ for server-side filtering
Explicit memory: save_memory / recall_memory tools for high-value facts

Slack Integration Pattern

API Gateway → Lambda (3s ack) → SQS → Worker Lambda (15min) → AgentCore
Session ID: thread-based for shared context, channel+user for non-thread
Actor ID: Slack user ID for memory isolation