Comprehensive collection of AI agent design patterns, architectures, and implementation guides
Comprehensive overview of the foundational components and layers that make up modern AI agent architectures.
Explore ArchitectureVisual mapping of system components, data flows, and architectural patterns for AI agent implementations.
View Architecture MapDetailed blueprint for integrating AI agents with existing systems and infrastructure components.
See Integration GuideIn-depth exploration of individual agent components, their responsibilities, and implementation patterns.
Deep DiveUnderstanding how agents store, retrieve, and manage different types of memory and knowledge.
Explore MemoryComprehensive taxonomy of tools and their integration patterns within AI agent ecosystems.
View ToolsNavigation guide for designing and implementing complex multi-agent systems and workflows.
Navigate SystemsProven patterns and best practices for implementing multi-agent architectures and coordination.
View PatternsCollection of workflow examples showcasing different multi-agent collaboration patterns.
Browse WorkflowsUnderstanding how agents make decisions using Retrieval-Augmented Generation and feedback loops.
Explore Decision FlowVisual decision trees and branching logic patterns for agent decision-making processes.
View Decision TreesDetailed analysis of the underlying mechanics and implementation details of common agent patterns.
Deep DiveComprehensive periodic table of prompting techniques, patterns, and best practices for AI agents.
Explore PromptsPractical playbook with design principles, guidelines, and templates for creating effective AI agents.
View PlaybookInteractive visualization of reactive loops and feedback mechanisms in agent architectures.
See VisualizationFramework for evaluating and validating structured outputs from AI agents and their reliability.
View FrameworkComprehensive framework for handling, categorizing, and recovering from validation errors in agent systems.
Explore Framework