Building autonomous AI agents capable of reasoning, retrieving knowledge, and executing tasks. Covers AI agent architectures, memory persistence, vector search integration, API calling, and real-world applications in customer support, e-commerce, and automation.
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MCP (Model Context Protocol) is a new open protocol designed to standardize how applications provide context to Large Language Models (LLMs).
Think of MCP like a USB-C port but for AI agents: it offers a uniform method for connecting AI systems to various tools and data sources.
This post breaks down MCP, clearly explaining its value, architecture, and how it differs from traditional APIs.
Jupyter Agent automates Jupyter Notebook creation, making it easy to analyze datasets before vectorizing them for Retrieval-Augmented Generation (RAG).
In this guide, we'll use Jupyter Agent Hugging Face space to explore a fictional shoe store's dataset, uncover key insights, and prepare the data for an AI-powered search system.
When an e-commerce customer asks for something like women's black shoes with red details, naive chatbots often struggle to apply multiple color filters simultaneously.
In this post, learn how to build a more advanced AI agent using Python, Pinecone and LlamaIndex, complete with metadata filtering and real-world code examples to handle multi-color product queries seamlessly.
This December, I'm highlighting how naive chatbots fail at budget-focused inquiries and how AI agents get it right.
We've seen naive chatbots fail at clarifying questions, context shifts, numerical requirements, multiple requests in one query, price filters, style suggestions, unavailable colors, negations, new color requirements, multi-color requests and special occasions.
In yesterday's issue, we saw how AI agents handle multiple product requests in a single query.
Today, we focus on a budget-focused searches.
A customer says, "Do you have blue sports shoes under $60?"
A naive chatbot may ignore the price constraint or claim none exist. An AI agent, on the other hand, applies both color and price filters, returning exactly what the user needs.
This December, I'm highlighting how naive chatbots fail at numeric filters and how AI agents get it right.
We've seen naive chatbots fail at clarifying questions, context shifts, numerical requirements, multiple requests in one query, price filters, style suggestions, unavailable colors, negations, new color requirements, multi-color requests and special occasions.
In yesterday's issue, we explored numeric filters for lower heel heights.
Today, we look at a new scenario: a user wants "red heels" and "blue men's sneakers" in the same message.
A naive chatbot may only focus on one part of the request or return random mismatched products. An AI agent, however, will parse both requests and provide accurate matches for each.
This December, I'm highlighting how naive chatbots fail at numeric filters and how AI agents get it right.
We've seen naive chatbots fail at clarifying questions, context shifts, numerical requirements, multiple requests in one query, price filters, style suggestions, unavailable colors, negations, new color requirements and multi-color requests.
Yesterday, we saw how AI agents handle formal requirements. Today, we focus on a request for lower heel heights.
When a customer says, "I need women's heels with a heel height less than 2 inches", a naive chatbot might find only one option or ignore some products.
An AI agent, however, uses numeric filtering to find all matches.
This December, I'm showing how naive chatbots fail and how AI agents make shopping easier.
We've seen naive chatbots fail at clarifying questions, context shifts, numerical requirements, multiple requests in one query, price filters, style suggestions, unavailable colors, negations, new color requirements and multi-color requests.
Today, we focus on how agent handle special occasions.
When a customer says, "I need women's shoes for a gala night", a naive chatbot returns casual items.
An AI agent, however, understands the need for formal shoes, filtering the database to find elegant heels perfect for the occasion.
This December, I'm showing how naive chatbots fail and how AI agents make shopping easier.
We've seen naive chatbots fail at clarifying questions, context shifts, numerical requirements, multiple requests in one query, price filters, style suggestions, unavailable colors, negations and new color requirements.
Today, we focus on multi-color requirements.
When a customer wants "women's black shoes with blue details" a naive chatbot can't handle the combined color request. It either returns no results or random items. An AI agent, on the other hand, understands the request fully and finds the exact product that matches both colors.
This December, I'm showing how naive chatbots fail and how AI agents make shopping easier.
We've seen naive chatbots fail at clarifying questions, context shifts, numerical requirements, multiple requests in one query, price filters, style suggestions, unavailable colors and negations.
Today, we focus on remembering color requirements while keeping the original product constraints intact.
When a customer first asks for men's running shoes and then follows up with a color preference, a naive chatbot might forget the original request. An AI agent, however, remembers both the category and the color, delivering results that match the entire conversation.
This December, I'm showing how naive chatbots fail and how AI agents make shopping easier.
We've seen naive chatbots fail at clarifying questions, context shifts, numerical requirements, multiple requests in one query, price filters, style suggestions and unavailable colors.
Today, the customer wants men's casual shoes but not in black. The naive chatbot still includes black shoes.
The AI agent, however, filters out black shoes and shows other colors.