1️⃣ System Prompt: Define your agent’s role, capabilities, and boundaries. This gives your agent the necessary context.
2️⃣ LLM (Large Language Model): Choose the engine. GPT-5, Claude, Mistral, or an open-source model — pick based on reasoning needs, latency, and cost.
3️⃣ Tools - Equip your agent with tools: API access, code interpreters, database queries, web search, etc. More tools = more utility. Max 20.
4️⃣ Orchestration: Use frameworks (like LangChain, AutoGen, CrewAI) to manage reasoning, task decomposition, and multi-agent collaboration.
5️⃣ Memory: Implement both short-term (context window) and long-term memory (Vector DBs like Pinecone, Weaviate, Chroma).
6️⃣ UI (User Interface): Design an intuitive chat UI or business automation workflow interface that enables smooth interaction with your agent (and automated actions).
7️⃣ AI Evals: Test your agent's performance with real-world tasks. Use tools like TruLens, Rebuff, or custom evals to measure effectiveness, reliability, and safety.
I have one more thing before you go.
If you want to become a generative AI data scientist in 2025 ($200,000 career), then I'd like to help:
On Wednesday, October 29th, I'm sharing one of my best AI Projects:

How I built an AI Customer Segmentation Agent with Python
Understanding P-Values is essential for improving regression models.
In 2 minutes, I'll crush your confusion.
1. The p-value:
A p-value in statistics is a measure used to assess the strength of the evidence against a null hypothesis.
2. Null Hypothesis (H₀):
The null hypothesis is the default position that there is no relationship between two measured phenomena or no association among groups. For example, under H₀, the regressor does not affect the outcome.
Understanding probability is essential in data science.
In 4 minutes, I'll demolish your confusion.
Let's go!
1. Statistical Distributions:
There are 100s of distributions to choose from when modeling data. Choices seem endless. Use this as a guide to simplify the choice.
2. Discrete Distributions:
Discrete distributions are used when the data can take on only specific, distinct values. These values are often integers, like the number of sales calls made or the number of customers that converted.
🚨BREAKING: New Python library for agentic data processing and ETL with AI
Introducing DocETL.
Here's what you need to know:
1. What is DocETL?
It's a tool for creating and executing data processing pipelines, especially suited for complex document processing tasks.
It offers:
- An interactive UI playground
- A Python package for running production pipelines
2. DocWrangler
DocWrangler helps you iteratively develop your pipeline:
- Experiment with different prompts and see results in real-time
- Build your pipeline step by step
- Export your finalized pipeline configuration for production use