A Shift in Intelligent Workflow Development:

AI is advancing faster than ever before, from proactive participating chatbots and now to thinking systems. A recent online Expert Session on “Agentic AI” gave students of Data Science and AI/ML a look into how the intelligent automation is evolving.

Coordinated as part of the AI/ML learning series, this expert talk introduced participants to Agentic AI, which is a more robust category of AI systems that do not just *respond*, but can *reason, act, and adapt* using tools and APIs off-The-Shelf.

What is Agentic AI? 

Agentic AI is a future iteration of what traditional and generative models were. Instead of a chatbot limited to answering questions based on its training, Agentic AI agents can think, decide, and utilize real-world tools to retrieve live data, run calculations, or automate processes. 

The presenter articulated it very well:

“Agentic AI = Think + Act + Re-Act.” 

This combination blends independence and reasoning ability together - which is what it means for AI agents to interact with solving actual problems with real data.

Engaging with LangFlow: Making Your First AI Agent

One of the major highlights of this session was an in-action demonstration using LangFlow, a visual builder that allows anyone to start programming AI agent workflows without significant coding experience. 

Students discovered:

• Integrating OpenAI GPT models (for example, asked about GPT-4o-mini) for reasoning 
• Connecting to an API to the Tavily Search API to pull live results from the web 
• Building a Chat Assistant Agent that can pull real-time and summarize current information 

The demo workflow - Chat Input → Agent → Tavily API → Chat Output - was visible to the students and helped them visualize the decision-making process each step.

Tools and Technologies Implemented

The ease of a visual representation of connecting the APIs made even those new to AI/ML feel comfortable.

Importance for Data Science Learners

For learners of Data Analysis, Machine Learning, and AI, Agentic AI expands your view and opens new doors.

Here are a few ways:

• Real-Time Data: Agents can source data from the web for up-to-date information - an important resource for live dashboards or news analytics.
• Data Automation: Students can create agents that analyze Excel or CSV datasets independently.
• RAG (Retrieval-Augmented Generation) Skills: RAG teaches one way to integrate knowledge bases with reasoning models, which is an important skill for deploying data-driven AI systems.
• Career Path Relevance: There are workflows like “AI Data Analysts” or “AI Productivity Assistants,” that not only give you experience of the technology, but also mirror what businesses are deploying for business analytics, research and automation.

Bonus Showcase of Demonstrations for Gen Z Learners

The session also presented inventive Agentic AI ideas students could try:

• AI Research Analyst – pulling scholarly conversations from Wikipedia
• AI Travel Planner – merging weather and location information through APIs
• AI Learning Assistant – pulling trending educational tutorial videos from YouTube
• AI News Curator – the summarization of daily tech news
• AI Data Analyst – reading and summarize a local Excel/CSV data set

These examples illustrated how Agentic AI could be enjoyable, interactive, and relevant at the same time.

Key Takeaways

By the conclusion of the session, students were able to:

✅ Comprehend the structure of Agentic AI systems

✅ Build functional AI workflows by visually connecting APIs and models

✅ Implement intelligent agents that think and act autonomously

✅ Acquire hands-on experience relevant to modern-day data-centric jobs.

Conclusion

The expert session was more than a demonstration of technical capabilities - it was a glimpse into the future of intelligent automation. For those students exploring Data Analysis, AI, or Machine Learning, understanding what is meant by Agentic AI is about understanding the relationship between human reasoning and intelligent machine capacity.

For platforms like LangFlow, the ability to create your own intelligent AI assistant is literally a few clicks away - and requires no heavy coding.

The session ended on an inspiring note:

"AI engineers of tomorrow will not only be in the business of building models, but also agents that build solutions."