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Agentic System

Overview

The Agentic System is a sophisticated component of our AI Marketplace platform that enables the creation and management of autonomous AI agents. These agents can perform complex tasks, make decisions, and interact with various services and APIs.

Key Features

  • 🤖 Autonomous Agents
  • Goal-oriented behavior
  • Decision-making capabilities
  • Self-improvement mechanisms

  • Task planning and execution

  • 🔄 Tool Integration

  • API integration framework
  • Custom tool development
  • Tool discovery and management

  • Usage monitoring and optimization

  • 🧠 Learning Capabilities

  • Reinforcement learning
  • Experience replay
  • Performance optimization
  • Adaptive behavior

Architecture

graph TD
    A[Agent Manager] --> B[Task Planner]
    A --> C[Tool Registry]
    A --> D[Memory Store]
    B --> E[Action Executor]
    C --> E
    D --> B
    E --> F[Result Analyzer]
    F --> D
    F --> G[Learning Module]
    G --> B

Getting Started

Prerequisites

  • Python 3.8+ for SDK usage
  • Understanding of agent-based systems
  • API key for authentication

Quick Start

  1. Install the SDK: 

    pip install ai-marketplace-agentic

  2. Create an agent: 

    from ai_marketplace.agentic import Agent

agent = Agent(
    name="research_assistant",
    capabilities=["web_search", "document_analysis"],
    goals=["gather_information", "summarize_findings"]
)

  3. Define agent behavior: 

    @agent.action
def search_web(query):
    # Implement web search logic
    return results

@agent.action
def analyze_document(text):
    # Implement document analysis
    return analysis

  4. Run the agent: 

    result = agent.run(
    task="Research AI trends and create a summary",
    constraints={"time_limit": "1h"}
)

Best Practices

  1. Agent Design
  2. Define clear goals and constraints
  3. Implement proper error handling
  4. Use appropriate tools for tasks

  5. Monitor agent behavior

  6. Tool Integration

  7. Validate tool outputs
  8. Implement rate limiting
  9. Handle API failures gracefully

  10. Cache frequently used results

  11. Performance Optimization

  12. Optimize tool selection
  13. Implement parallel processing
  14. Use efficient data structures
  15. Monitor resource usage

Advanced Features

Custom Tool Development

from ai_marketplace.agentic import Tool

class CustomTool(Tool):
    def __init__(self):
        super().__init__(
            name="custom_tool",
            description="Performs custom operations"
        )

    def execute(self, params):
        # Implement tool logic
        return result

Memory Management

# Configure agent memory
agent.configure_memory(
    short_term_capacity=1000,
    long_term_storage="vector_db",
    retrieval_strategy="semantic"
)

Learning Configuration

# Set up learning parameters
agent.configure_learning(
    algorithm="reinforcement",
    reward_function=custom_reward,
    exploration_rate=0.1
)

Monitoring and Analytics

  • Agent performance metrics
  • Tool usage statistics
  • Learning progress tracking
  • Resource utilization

Support and Resources