This course is a later course in the IBM RAG and Agentic series. The 12m intro lecture is a remarkable performance - one of the best I have encountered. None of the Skills Network labs are functioning. I plan to return to the course and retake it once it is published. In the meantime, plan to complete the requisite courses here on Coursera, BFP

Great course on fundamentals. Good practical exercises.

It was made easy and lab launch added so much of hands-on knowledge. Only if you could make it available for life-time access.

It provides enough general and specific knowledge to create complex AI Agents. A great entry point to Agentic AI

This course exceeded my expectations and proved to be an outstanding investment in my professional development

Greate course . Helped me see more practical options when developing the Agentic workflow

Excelente curso, con muchos recursos y bien explicado con profundidad.

Awesome for solid theory and ai usecases practicing!

Really useful and what I was expecting

Excelente en todo.

Excellent

Good

For context, I am a seasoned front-end developer. The first course is okay, but it took a while to understand what the labs were and how it all worked. One has to spend some time discovering it all out. By the third module I was happy that I was cruising through it. In itself the information is quite helpful if you're new to the concept of AI development.

Great simple & easy course, but some Lab's code snippets had typos/issues ;)

GOOD

While the course provides a strong conceptual foundation, several labs and lessons contain outdated practices, security risks, and technical failures that significantly hinder the learning experience: 1. General Outdated API References (LangChain v1/v2 Migration) * Issue: Across multiple lessons and labs, the curriculum relies on API calls and library structures that have since been deprecated or moved. * Impact: Students using current LangChain versions encounter constant deprecation warnings or outright errors, creating unnecessary friction for those trying to apply these skills in modern environments. 2. YouTube Lab: ToS Violations & Technical Failures * Legal Risk: This lab relies on third-party scraping tools (yt-dlp, youtube-transcript-api) to fetch data. This is a direct violation of YouTube’s Terms of Service (Section 4) regarding automated access. * Technical Failure: These tools are currently broken due to YouTube’s anti-bot measures. Students face immediate 429 Rate Limits or Sign-in prompts, making the lab impossible to complete as intended. 3. Data Visualization Lab: High-Risk Implementation * Security Risk: Although the course correctly labels the Pandas Agent as "experimental," and "not for use in production" its reliance on PythonREPL still presents massive "RCE by Design" (Remote Code Execution) security concerns. * Recommendation: While the focus on Natural Language Interfaces (NLI) is excellent, the curriculum should move toward Structured Tool Calling and Sandboxed Code Execution (e.g., E2B or Docker). Relying on a "blank check" code execution agent is no longer considered a best practice in AI engineering. Conclusion: The course offers great high-level insights, but the technical implementations need a major update to reflect current security standards, legal compliance, and stable API patterns.

when having videos that people write on screen or sketch, please make writing more clear , not messy and sloppy. Also, please speak more clearly since this is an international course

OUTDATED! This course is based on LangChain syntax FROM 2023-24 and wasn't updated to v1.0!Otherwise, good theoretical aspects but too verbose

outdated, plus the lab notebooks did not load the models

Labs had various technical issues

could have been better

Theorical non practical