AI Agents: Development, Implementation, and Optimization

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AI agents are transforming automation, decision-making, and user interactions. Below is a structured approach to building and refining AI agents, along with practical commands and code snippets.

You Should Know:

1. Define & Plan

Before coding, outline the agent’s objectives:

  • Objective: Automate customer support, data scraping, or workflow orchestration.
  • Data Sources: APIs (REST, GraphQL), databases (PostgreSQL, MongoDB), or web scraping (BeautifulSoup, Scrapy).

Example Linux Command (API Testing with `curl`):

curl -X GET "https://api.example.com/data" -H "Authorization: Bearer YOUR_TOKEN"

Python Code (Web Scraping with `requests` and `BeautifulSoup`):

import requests 
from bs4 import BeautifulSoup

url = "https://example.com" 
response = requests.get(url) 
soup = BeautifulSoup(response.text, 'html.parser') 
print(soup.title.text)

2. Begin Development

Choose a framework:

  • Simple Tasks: LangChain, AutoGPT
  • Complex Workflows: TensorFlow, PyTorch

Docker Setup for AI Agent (Containerization):

docker run -it --name ai-agent python:3.9-slim 
pip install langchain openai

3. Collect & Store Data

Connect to databases or APIs:

PostgreSQL Command:

psql -h localhost -U user -d dbname -c "SELECT  FROM datasets;"

Python (API Data Fetching):

import requests 
import json

api_url = "https://api.example.com/fetch" 
response = requests.get(api_url) 
data = json.loads(response.text) 
print(data)

4. Provide Memory

Use vector databases (Redis, Pinecone) for AI memory.

Redis CLI (Memory Caching):

redis-cli SET "ai_session:123" "user_preferences" 
redis-cli GET "ai_session:123"

5. Test, Monitor & Optimize

Linux Monitoring (Check CPU/Memory):

top 
htop

Automated Testing (Python `unittest`):

import unittest

class TestAIAgent(unittest.TestCase): 
def test_response_time(self): 
self.assertLess(get_api_response_time(), 1.0)

if <strong>name</strong> == "<strong>main</strong>": 
unittest.main()

What Undercode Say:

AI agents require structured planning, robust data pipelines, and continuous monitoring. Use Docker for deployment, PostgreSQL for structured data, and Redis for session memory. Always validate API interactions and automate testing.

Prediction:

AI agents will increasingly integrate with low-code platforms, reducing development time while improving real-time decision-making. Expect more AI-driven cybersecurity agents for threat detection.

Expected Output:

A functional AI agent with automated data collection, memory retention, and real-time monitoring.

Relevant URLs:

IT/Security Reporter URL:

Reported By: Naresh Kumari – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅

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