5 AI Agent Tasks for Learners
Picture by Editor | ChatGPT
Introduction
Agentic AI is a scorching matter proper now. These are instruments that not solely reply questions however also can plan, cause, and take motion utilizing varied instruments and APIs. If you’re on this technological shift and in search of a sensible solution to get began, this information is for you.
We are going to stroll you thru 5 beginner-friendly AI agent initiatives which can be straightforward to copy, require minimal setup, and don’t necessitate superior coding expertise.
1. Picture Collage Generator with ChatGPT Brokers
ChatGPT Brokers are AI assistants able to considering and performing independently. They will proactively choose instruments and make the most of a built-in digital pc to finish duties from begin to end.
To allow Agent Mode in ChatGPT, observe the information supplied in OpenAI’s Assist Heart to get began.
For this challenge, present ChatGPT with a transparent immediate to open https://openai.com/index/introducing-chatgpt-agent/. Ask it to assemble all of the benchmark pictures from that web page, organize them right into a 16:9 collage, and draw pink define rectangles across the columns that show the agent’s outcomes. No technical expertise are required, simply persistence and some follow-up prompts to refine the alignment and export the ultimate picture.
Undertaking information: ChatGPT Agent
Picture Collage Generator with ChatGPT Brokers
2. Language Tutor with Langflow
Langflow is a low-code, visible builder for creating agentic and retrieval-augmented technology (RAG) functions: you drag and drop elements to assemble “flows” that join LLMs, instruments, and knowledge sources, then take a look at and share them in a visible editor.
On this challenge, you’ll use Langflow to construct a easy language-learning assistant that generates quick studying passages utilizing solely the learner’s identified vocabulary. The agent can add new phrases by way of a instrument, one other element masses the present vocabulary, and a story-generation instrument creates texts constrained to these phrases, all orchestrated by a most important chat agent.
Undertaking information: Langflow: A Information With Demo Undertaking
Language Tutor with Langflow
3. Information Analyst with Flowise
Flowise is an open-source visible builder designed for AI brokers and huge language mannequin (LLM) workflows. It permits customers to create functions by assembling prompts, fashions, instruments, and knowledge connectors into drag-and-drop nodes, enabling fast prototyping and deployment of clever functions.
On this challenge, you’ll create an information analyst agent that connects to a SingleStore database. The agent makes use of a customized code node (mysql2/promise) to explain the desk and extract schema data. It then feeds this knowledge, together with the consumer’s query, right into a immediate and LLM chain to generate an SQL question. The question is executed in one other code node, and the agent returns a transparent reply that features each the SQL question and the outcomes, all inside a Flowise chat circulate.
Undertaking information: Flowise: A Information With Demo Undertaking
Information Analyst with Flowise
4. Medical Prescription Analyzer with Grok 4
Grok 4 is xAI’s flagship mannequin, obtainable by the xAI API. It comes with superior imaginative and prescient reasoning, perform calling, and native instrument integrations.
On this challenge, we’re growing a medical prescription analyzer. Grok 4 will analyze prescription pictures to extract the names of medicines. It would invoke Firecrawl instruments, both individually or concurrently, to retrieve descriptions, costs, and hyperlinks. The outcomes will then be compiled right into a clear markdown report. A light-weight Gradio consumer interface will enable customers to add a picture, view streaming logs, and entry the ultimate abstract.
Undertaking information: Constructing a Medical AI Software with Grok 4
Medical Prescription Analyzer with Grok 4
5. Customized AI Agent with LangGraph and llama.cpp
LangGraph means that you can construct dependable, tool-using brokers as graphs, whereas llama.cpp affords a quick, native LLM runtime with an OpenAI-compatible server, making it ideally suited for low-latency, on-device workflows.
On this challenge, you’ll arrange llama.cpp’s llama-server utilizing a Gemma 3 4B IT GGUF mannequin. Then, you’ll configure LangChain‘s ChatOpenAI to level to http://localhost:8000/v1. Lastly, you’ll use LangGraph’s create_react_agent to attach a ReAct agent with instruments like Tavily search and a Python REPL. The result’s a neighborhood agent able to looking for up-to-date data and executing code, all powered by your self-hosted mannequin backend.
Undertaking information: Constructing AI Brokers with llama.cpp
Customized AI Agent with LangGraph and llama.cpp
Wrapping Up
I strongly consider in studying by expertise. I encourage my college students to construct initiatives as a result of this method accelerates understanding, gives hands-on expertise, and, most significantly, helps them create a portfolio that draws actual alternatives.
Even for those who come from a enterprise background, you may nonetheless undertake these initiatives. Each contains hyperlinks to guides and clear steps that can assist you get began.
After you full all 5 initiatives, don’t cease there:
- Share your work together with your community.
- Ask for suggestions and make enhancements.
- Add the initiatives to your portfolio and resume.
