AI Agent

AI Agent

Overview

The AI Agent pattern builds intelligent, memory-enhanced conversational systems that learn, adapt, and maintain context across interactions. This pattern combines large language model reasoning with persistent memory capabilities to create agents that improve over time through accumulated knowledge and experience.

Use this pattern when building:

• Personal assistants with long-term memory
• Domain-specific expert systems
• Customer support agents that learn from interactions
• Research assistants that build knowledge over time
• Training or educational systems with personalized guidance

Architecture Diagram

flowchart TB
    User[User Input]
    ServiceEntry[Service Entry Point]

    User --> ServiceEntry

    subgraph Memory ["Memory Retrieval"]
        SmartMemory[SmartMemory]
        Procedural["Procedural Memory<br/>System Prompts"]
        Episodic["Episodic Memory<br/>Past Interactions"]
        Semantic["Semantic Memory<br/>Domain Knowledge"]
        Working["Working Memory<br/>Session Context"]

        ServiceEntry --> SmartMemory
        SmartMemory --> Procedural
        SmartMemory --> Episodic
        SmartMemory --> Semantic
        SmartMemory --> Working
    end

    subgraph Processing ["AI Processing"]
        AI["AI Model 70b+"]

        Procedural --> AI
        Episodic --> AI
        Semantic --> AI
        Working --> AI
        ServiceEntry --> AI
    end

    subgraph Response ["Response & Learning"]
        GeneratedResponse[Generated Response]
        Learning[Learning Updates]

        AI --> GeneratedResponse
        AI --> Learning
        Learning --> SmartMemory
        GeneratedResponse --> ServiceEntry
    end

    ServiceEntry --> User

Components

• AI - Large language model (70b+ recommended) for reasoning and response generation
• SmartMemory - Four-layer memory system enabling learning and context maintenance
• Service - Orchestration layer managing user interactions and memory coordination

Logical Flow

1. Context Assembly - Service retrieves relevant context from SmartMemory subsystems (procedural prompts, episodic history, semantic knowledge)

2. Enhanced Prompt Construction - Service combines user input with retrieved memory context for rich, contextual prompts

3. AI Processing - AI model processes enhanced prompt using current input and historical context for advanced reasoning

4. Response Generation - AI generates responses reflecting immediate needs and accumulated understanding

5. Memory Updates - Service updates SmartMemory with interaction outcomes across all memory types

6. Continuous Learning - Each interaction contributes to growing intelligence through systematic memory updates

Implementation

1. Deploy Service Component - Configure orchestration service with session management and memory integration

2. Configure AI Model - Select 70b+ parameter model with appropriate context windows and response parameters

3. Initialize SmartMemory - Set up memory subsystems with initial procedural prompts and seed knowledge

4. Define Agent Behavior - Populate procedural memory with system prompts defining personality and interaction patterns

5. Production Setup - Add authentication, monitoring, and external knowledge source integration for production deployments

raindrop.manifest

Basic Agent

raindrop.manifest

application "ai-agent" {

  service "agent_service" {
  }

  ai "reasoning_engine" {
  }

  smartMemory "agent_memory" {
  }
}

Advanced Agent

raindrop.manifest

application "advanced-ai-agent" {

  service "agent_service" {
  }

  ai "reasoning_engine" {
  }

  ai "analysis_engine" {
  }

  smartMemory "agent_memory" {
  }

  observer "agent_monitor" {
  }
}

Best Practices

• Use procedural memory consistently - Store stable system prompts, personality definitions, and behavioral guidelines
• Use episodic learning - Regularly flush working memory to episodic storage for long-term pattern learning
• Organize semantic knowledge - Structure domain knowledge hierarchically for efficient retrieval
• Monitor memory growth - Implement retention policies and cleanup to maintain performance
• Choose appropriate model size - Use 70b+ models for advanced reasoning capabilities
• Tune temperature carefully - Lower values (0.3-0.5) ensure consistency while allowing adaptability
• Optimize context usage - Balance memory context inclusion with available context window
• Implement session caching - Cache frequently accessed memory contexts to reduce latency
• Use async memory updates - Update memory stores asynchronously to minimize user-facing latency