We’re shifting from chatbots to more advanced AI agents. For students and aspiring engineers, this is a great chance to stand out. Recruiters now want people who can design systems that reason, plan, and act, not just call APIs. In this article, I’ll share four Agentic AI projects you can build to boost your portfolio.
Agentic AI Projects to Upgrade Your Portfolio
Here are four projects that will not only teach you the cutting edge of Agentic AI but will make your portfolio stand out in a crowded market.
Build an LLM App with Reasoning Skills
People often use Large Language Models as advanced autocomplete tools. However, new methods like Chain of Thought prompting and reasoning models let us slow the model down and encourage it to think before answering.
You could create a math tutor or a code debugging assistant that not only gives answers but also explains each step along the way.
Working on these projects shows you understand the difference between prompt engineering and model capability. It also proves you care about accuracy and clear explanations, which are important for real-world AI. Find a guided project here.
Building an Agentic RAG Pipeline
Standard Retrieval-Augmented Generation works in a fixed way: the user asks a question, the system searches the database, and then gives an answer. But what happens if the data is messy or the question isn’t clear?
You can build a research assistant that searches a set of documents, notices when it needs more information, and then tries searching again with better keywords.
RAG is becoming a common project. Agentic RAG shows you can handle messy, real-world data. It proves you know how to build resilient systems, not just ones that work. Find a guided project here.
Build a Multi-Agent System With LangGraph
A single, large prompt can’t do everything. Just as a software team has a Product Manager, a Coder, and a Tester, complex AI apps need specialized agents working together.
For example, you can build an automated newsletter generator, where:
- Agent A (Researcher): Scrapes the web for news on a topic.
- Agent B (Writer): Summarizes the news into a draft.
- Agent C (Editor): Critiques the draft and sends it back to the Writer if it’s too long or boring.
This teaches you about State Management in AI. You’re not just chaining calls; you’re managing a workflow. This is how enterprise software is really built, which makes you more hireable for backend AI roles. Find a guided project here.
Build a Real-Time AI Assistant Using RAG + LangChain
Latency is important. A great answer that takes 30 seconds to generate is useless in a voice conversation or a customer support chat.
You can create a voice-enabled (or chat-based) customer support bot for a fictional e-commerce store that can look up order status in real-time.
These projects bridge the gap between Data Science and Software Engineering. They show you understand system architecture, latency, and user experience (UX), skills that are rare and highly valued in full-stack AI engineers. Find a guided project here.
Closing Thoughts
So, here are 4 Agentic AI Projects you should build to upgrade your portfolio:
- Build an LLM App with Reasoning Skills
- Building an Agentic RAG Pipeline
- Build a Multi-Agent System With LangGraph
- Build a Real-Time AI Assistant Using RAG + LangChain
If you found this article useful, you can follow me on Instagram for daily AI tips and practical resources. You might also like my latest book, Hands-On GenAI, LLMs & AI Agents. It’s a step-by-step guide to help you get ready for jobs in today’s AI field.
