Cognitive Architecture

Purpose: Patterns for the cognitive components of AI agents: Memory, Knowledge (RAG), and Reasoning.

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When to Use This Skill

• Designing Memory Systems (Conversation history, User profiles, Entity tracking).
• Building RAG Pipelines (Chunking, Embedding, Retrieval, Reranking).
• Managing Agent State across sessions.
• Selecting Vector Databases for knowledge retrieval.

Table of Contents

1. Cognitive Components
2. RAG Patterns
3. Memory Architectures
4. References

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Cognitive Components

A complete agent "brain" consists of three layers:

1. Context (Short-term Memory): The active context window (conversation history).
2. Knowledge (Long-term Memory/RAG): Static facts retrieved from vector stores or databases.
3. State (Episodic Memory): Structured data about the user or task progress persisted indefinitely.

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Scripts

Script	Purpose	Usage
scaffold-cognitive.py	Scaffold RAG pipeline and/or Memory system modules	python scaffold-cognitive.py --name my-agent --component all

Options:

• --component rag - RAG only (ingestion + retrieval + tests)
• --component memory - Memory only (short-term + long-term + tests)
• --component all - Both (default)
• --vector-store azure-ai-search - Use Azure AI Search instead of ChromaDB

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Reference Patterns

Pattern	Description	File
RAG Pipeline	Standard for ingesting and retrieving knowledge.	pattern-rag-pipeline.md
Memory System	Schema for short-term and long-term memory.	pattern-memory-systems.md

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Troubleshooting

Issue	Solution
Hallucinations	Increase retrieval "groundedness" threshold or reduce top_k.
Context Window Overflow	Implement "Summarization" strategy for conversation history.
Slow Retrieval	Use "Hybrid Search" (Keyword + Semantic) with filtered metadata.