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Understanding the Target Audience for Building a Self-Adaptive AI Agent
The target audience for this coding implementation tutorial includes software developers, data scientists, and business professionals interested in AI applications. They seek to enhance their skills in building intelligent systems that can adapt and learn from their processes.
Pain Points
- Lack of practical resources on implementing adaptive AI agents.
- Difficulty in integrating advanced AI frameworks like Google Gemini into existing workflows.
- Need for clear, actionable examples to understand complex concepts.
Goals
- To gain hands-on experience with the SAGE framework and Google Gemini API.
- To develop self-improving AI agents capable of dynamic task management.
- To apply learned concepts to real-world business problems.
Interests
- Innovative AI technologies and frameworks.
- Automation and optimization of business processes.
- Community engagement through collaborative learning platforms.
Communication Preferences
- Clear, concise, and structured tutorial formats.
- Visual aids and code snippets that enhance understanding.
- Interactive content such as forums or Q&A sections for community support.
Building a Self-Adaptive Goal-Oriented AI Agent Using Google Gemini and the SAGE Framework
In this tutorial, we explore the development of an advanced AI agent system based on the SAGE framework, which stands for Self-Adaptive Goal-oriented Execution. This implementation utilizes Google’s Gemini API. We will walk through the core components of the framework: Self-Assessment, Adaptive Planning, Goal-oriented Execution, and Experience Integration. By integrating these elements, we aim to create an intelligent, self-improving agent capable of breaking down high-level goals, planning actionable steps, methodically executing tasks, and learning from outcomes.
Key Components of the SAGE Framework
Self-Assessment
The first step is to evaluate the current state and capabilities of the AI agent. This involves assessing the progress score, available resources, knowledge gaps, potential risks, and recommendations for next steps.
Adaptive Planning
Based on the self-assessment, the agent generates a dynamic, context-aware task decomposition. This phase results in a list of actionable tasks with defined priorities and dependencies.
Goal-oriented Execution
This phase focuses on executing the defined tasks with precision. The AI agent breaks down tasks into concrete actions, executing each step methodically and validating results.
Experience Integration
After executing tasks, the agent learns from the outcomes. This integration process involves updating knowledge based on task execution results, capturing key insights, observed patterns, and necessary adjustments for future iterations.
Implementation Code
The following code example illustrates the implementation of the SAGE framework using Python:
import google.generativeai as genai
import json
import time
from typing import Dict, List, Any, Optional
from dataclasses import dataclass, asdict
from enum import Enum
class TaskStatus(Enum):
PENDING = "pending"
IN_PROGRESS = "in_progress"
COMPLETED = "completed"
FAILED = "failed"
@dataclass
class Task:
id: str
description: str
priority: int
status: TaskStatus = TaskStatus.PENDING
dependencies: List[str] = None
result: Optional[str] = None
def __post_init__(self):
if self.dependencies is None:
self.dependencies = []
class SAGEAgent:
def __init__(self, api_key: str, model_name: str = "gemini-1.5-flash"):
genai.configure(api_key=api_key)
self.model = genai.GenerativeModel(model_name)
self.memory = []
self.tasks = {}
self.context = {}
self.iteration_count = 0
# Further methods for self-assessment, adaptive planning, etc.
Conclusion
In this tutorial, we successfully implemented and executed a complete SAGE cycle with our Gemini-powered agent. The system demonstrated its ability to assess progress, dynamically generate actionable tasks, execute them efficiently, and refine its strategies based on learned experiences. This modular approach allows for further extension of the framework in complex, multi-agent environments or domain-specific applications.
For a complete code reference, please check the GitHub Page for tutorials, code, and notebooks. Follow us on Twitter and join our community for discussions and updates.
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