Architecture & Engineering Patterns

These design patterns provide a way to think about and structure the development of Agentic AI systems, drawing on existing research and established concepts. The choice of which patterns to use will depend on the specific requirements and goals of the AI agent or multi-agent system being developed.

Memory Management

How agents store and retrieve information

Short-Term Memory

Maintaining context within a single interaction or task.

Long-Term Memory

Storing and recalling information across multiple interactions or over time to personalize experiences.

Entity Memory

Specialized memory for maintaining detailed information about specific entities.

Team / Organization Memory

Aggregating the memory of related users within the boundary of a team or organization and storing it for further processing or planning by the agent.

Task Decomposition and Planning Patterns

How agents break down and address complex tasks.

Hierarchical Planning

Breaking down goals into sub-goals and actions at different levels of abstraction.

Means-Ends Analysis

Identifying the difference between the current state and the desired goal and selecting actions to reduce this difference.

Step-by-Step Planning

Creating a sequential plan where each step logically leads to the next.

Dynamic Planning

Adapting plans in response to changes in the environment or user inputs.

Tool Integration Patterns

Robust methods for agents to accurately and efficiently use external tools and APIs.

Generalized Agents with Generic Tools

Designing generic agents that can perform wide variety of tasks.

Specialized Agents with Specific Tools

Designing agents with a focused set of tools tailored to particular reasoning tasks.

Agent as an API

Reusing an agent through an API across multiple applications.