Agentic DesignPatterns and tools for production AI agents

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Contextual Structured Memory(CSM)

Memory organized in predefined, interpretable formats supporting symbolic reasoning and precise querying for multi-agent agentic AI systems

Complexity: highMemory Management

In 30 seconds

What: Organizes agent memory in predefined schemas with symbolic reasoning and precise querying, enabling structured knowledge graphs instead of unstructured text.
When to use: Multi-agent systems needing exact fact retrieval, filtering across many entities, or reasoning over relationships between structured data points.
Watch out: Schema design becomes a bottleneck; rigid structures break when real-world data doesn't fit predefined categories or requirements shift.

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Agentic DesignPatterns and tools for production AI agents

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🏗️

Contextual Structured Memory(CSM)

Memory organized in predefined, interpretable formats supporting symbolic reasoning and precise querying for multi-agent agentic AI systems

Complexity: highMemory Management

In 30 seconds

What: Organizes agent memory in predefined schemas with symbolic reasoning and precise querying, enabling structured knowledge graphs instead of unstructured text.
When to use: Multi-agent systems needing exact fact retrieval, filtering across many entities, or reasoning over relationships between structured data points.
Watch out: Schema design becomes a bottleneck; rigid structures break when real-world data doesn't fit predefined categories or requirements shift.

30-Second Overview

Pattern: Memory organized in predefined, interpretable formats supporting symbolic reasoning and precise queries

Why: Precise querying, formal logic reasoning, data integrity, consistent structured knowledge access

Key Insight: Schemas + Knowledge Graphs + Structured Queries + Symbolic Logic → Precise reasoning & consistency

Quick Implementation

1Define Schemas:Create predefined memory structures & formats

2Build Graph:Implement knowledge graph with typed relationships

3Enable Queries:Support precise SQL-like structured queries

4Add Reasoning:Implement symbolic logic & inference rules

5Share Structure:Enable cross-agent structured knowledge access

Example: schema_design → knowledge_graph → structured_queries → symbolic_reasoning → cross_agent_sharing

Do's & Don'ts

✅Use standardized schemas (JSON Schema, RDF, OWL) for consistency

✅Implement precise query capabilities with structured filters

✅Enable symbolic reasoning with formal logic rules

✅Maintain referential integrity across knowledge graphs

✅Version control schemas and support backward compatibility

❌Over-structure flexible data that benefits from loose schemas

❌Create overly complex schemas that are hard to understand

❌Ignore schema evolution and migration strategies

❌Use structured memory for highly dynamic unstructured content

❌Forget to validate data integrity when updating structures

When to Use

Use When

• Complex domain relationships need modeling
• Precise queries and reasoning required
• Formal logic and symbolic reasoning needed
• Data integrity and consistency critical
• Multi-agent knowledge sharing important

Avoid When

• Simple key-value storage needs
• Highly dynamic unstructured content
• Rapid prototyping with changing requirements
• Natural language processing tasks
• Real-time streaming data scenarios

Key Metrics

Schema Coverage

% domain concepts properly modeled

Query Precision

Accuracy of structured query results

Reasoning Correctness

% valid logical inferences

Data Integrity

Referential consistency score

Schema Utilization

% schema fields actively used

Cross-Agent Consistency

Shared structure alignment rate

Top Use Cases

Financial Analysis: Company financials, ratios, regulations in structured schemas (SQL-like queries: "SELECT companies WHERE pe_ratio < 25 AND sector = 'technology'")

Legal Systems: Laws, cases, precedents with formal relationship modeling (complex legal reasoning and precedent matching)

Scientific Databases: Research data with standardized ontologies and precise queries (materials science, drug discovery)

Enterprise Resource Planning: Business entities, processes, relationships in formal schemas (inventory, supply chain optimization)

Healthcare Records: Patient data, treatments, outcomes with medical ontologies (clinical decision support systems)

References & Further Reading

Deepen your understanding with these curated resources

Knowledge Representation Theory

Knowledge Representation and Reasoning (Brachman & Levesque, 2004)

Semantic Web Technologies: RDF, RDFS, OWL (Hitzler et al., 2019)

Description Logics: The Foundation of Semantic Web (Baader et al., 2003)

Formal Ontology in Information Systems (Guarino, 1998)

Implementation Frameworks

Apache Jena: Semantic Web Framework and Triple Store

Neo4j: Graph Database with Schema and Constraints

RDFLib: Python Library for Working with RDF Graphs

OWL API: Java API for Working with OWL Ontologies

Standards & Specifications

JSON Schema: Vocabulary for Annotating JSON Documents

RDF 1.1: Resource Description Framework Specification

OWL 2: Web Ontology Language Document Overview

SPARQL 1.1: Query Language for RDF

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Patterns

Design Patterns & Techniques

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30-Second Overview

Pattern: Memory organized in predefined, interpretable formats supporting symbolic reasoning and precise queries

Why: Precise querying, formal logic reasoning, data integrity, consistent structured knowledge access

Key Insight: Schemas + Knowledge Graphs + Structured Queries + Symbolic Logic → Precise reasoning & consistency

Quick Implementation

1Define Schemas:Create predefined memory structures & formats

2Build Graph:Implement knowledge graph with typed relationships

3Enable Queries:Support precise SQL-like structured queries

4Add Reasoning:Implement symbolic logic & inference rules

5Share Structure:Enable cross-agent structured knowledge access

Example: schema_design → knowledge_graph → structured_queries → symbolic_reasoning → cross_agent_sharing

Do's & Don'ts

✅Use standardized schemas (JSON Schema, RDF, OWL) for consistency

✅Implement precise query capabilities with structured filters

✅Enable symbolic reasoning with formal logic rules

✅Maintain referential integrity across knowledge graphs

✅Version control schemas and support backward compatibility

❌Over-structure flexible data that benefits from loose schemas

❌Create overly complex schemas that are hard to understand

❌Ignore schema evolution and migration strategies

❌Use structured memory for highly dynamic unstructured content

❌Forget to validate data integrity when updating structures

When to Use

Use When

• Complex domain relationships need modeling
• Precise queries and reasoning required
• Formal logic and symbolic reasoning needed
• Data integrity and consistency critical
• Multi-agent knowledge sharing important

Avoid When

• Simple key-value storage needs
• Highly dynamic unstructured content
• Rapid prototyping with changing requirements
• Natural language processing tasks
• Real-time streaming data scenarios

Key Metrics

Schema Coverage

% domain concepts properly modeled

Query Precision

Accuracy of structured query results

Reasoning Correctness

% valid logical inferences

Data Integrity

Referential consistency score

Schema Utilization

% schema fields actively used

Cross-Agent Consistency

Shared structure alignment rate

Top Use Cases

Financial Analysis: Company financials, ratios, regulations in structured schemas (SQL-like queries: "SELECT companies WHERE pe_ratio < 25 AND sector = 'technology'")

Legal Systems: Laws, cases, precedents with formal relationship modeling (complex legal reasoning and precedent matching)

Scientific Databases: Research data with standardized ontologies and precise queries (materials science, drug discovery)

Enterprise Resource Planning: Business entities, processes, relationships in formal schemas (inventory, supply chain optimization)

Healthcare Records: Patient data, treatments, outcomes with medical ontologies (clinical decision support systems)

References & Further Reading

Deepen your understanding with these curated resources

Knowledge Representation Theory

Knowledge Representation and Reasoning (Brachman & Levesque, 2004)

Semantic Web Technologies: RDF, RDFS, OWL (Hitzler et al., 2019)

Description Logics: The Foundation of Semantic Web (Baader et al., 2003)

Formal Ontology in Information Systems (Guarino, 1998)

Implementation Frameworks

Apache Jena: Semantic Web Framework and Triple Store

Neo4j: Graph Database with Schema and Constraints

RDFLib: Python Library for Working with RDF Graphs

OWL API: Java API for Working with OWL Ontologies

Standards & Specifications

JSON Schema: Vocabulary for Annotating JSON Documents

RDF 1.1: Resource Description Framework Specification

OWL 2: Web Ontology Language Document Overview

SPARQL 1.1: Query Language for RDF

Contextual Structured Memory (CSM) - Agentic Design