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👉 Define & Plan
Determine the agent’s objectives.
What issue will this AI address?
What steps should the AI take?
What tools will be utilized?
Where will the data originate from (e.g., websites, APIs)?
What should the user interaction be like?
👉 Begin Development
Select the platform or framework for building the AI.
Will the agent require intricate workflows or just simple tasks?
How will data be exchanged between systems?
👉 Collect & Store Data
Link the agent to external APIs, services, or databases to collect and store data that aids in task execution.
Which tools must the AI connect to (APIs, databases)?
👉 Provide Memory
Integrate memory to allow the AI to learn and remember.
How will the AI retain critical information?
What kind of memory does it require for decision-making?
👉 Test, Monitor & Optimize
Post-launch, continuously test, monitor, and refine the AI to ensure it works as intended.
Is the agent functioning as expected?
What changes can be made based on real-time performance data?
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What Undercode Say
Building AI agents requires a structured approach, starting with defining clear objectives and selecting the right tools. For instance, using Python with libraries like TensorFlow or PyTorch can simplify AI development. Here are some practical commands and codes to get started:
1. Install TensorFlow:
pip install tensorflow
2. Connect to an API using Python:
import requests
response = requests.get('https://api.example.com/data')
print(response.json())
3. Store Data in SQLite Database:
import sqlite3
conn = sqlite3.connect('ai_data.db')
c = conn.cursor()
c.execute('''CREATE TABLE IF NOT EXISTS data (id INTEGER PRIMARY KEY, info TEXT)''')
c.execute("INSERT INTO data (info) VALUES ('Sample Data')")
conn.commit()
4. Monitor System Performance on Linux:
top
5. Optimize AI Model with TensorBoard:
tensorboard --logdir=path/to/logs
For memory integration, consider using Redis for fast data retrieval:
6. Install Redis:
sudo apt-get install redis-server
7. Python Redis Example:
import redis
r = redis.Redis(host='localhost', port=6379, db=0)
r.set('key', 'value')
print(r.get('key'))
Testing and monitoring are crucial. Use tools like Prometheus and Grafana for real-time performance tracking. For example:
8. Install Prometheus:
sudo apt-get install prometheus
9. Install Grafana:
sudo apt-get install grafana
Finally, always refine your AI based on real-time data. Use A/B testing frameworks like Google Optimize to make data-driven decisions.
For further reading, visit:
By following these steps and using the provided tools, you can build, test, and optimize AI agents effectively.
References:
Hackers Feeds, Undercode AI
