[Season 2] Spring AI in Practice: Developing Multi-AI Agent Systems

Title: 🤖 Still reviewing & grading assignments manually? I leave it to Spring AI! (🎁Year-end half price)

Hello developers! Aren't you tired of repetitive code reviews and assignment grading?

So I've prepared this for you! An automated system where you push code to GitHub (PR) → AI analyzes it → grades and provides feedback - I've launched (2025-12-24) the [Spring AI in Action] Build Your Own 24/7 Code Reviewer & Auto-Grading Agent course that builds this from scratch.

✅Here's what you'll build!

• Webhook: My server detects GitHub events in real-time

• Spring AI: Give AI a "strict senior developer" persona

• Async Workflow: Review and grading simultaneously! (parallel processing optimization)

• Vaadin: Even a pretty dashboard for checking scores (no frontend knowledge needed)

🎄Year-End Gift: 50% Off!🎁 Enroll now and own it for life at half price.
Grow into a 'backend developer who can handle AI' next year!

👉Check out the course:https://inf.run/q8ofE
👉Implementation video demo: https://youtu.be/E9b24Y3GNDg

🎬 [Scenario] 24-Hour Coding Classroom with AI Teaching Assistant

1. 📢 Assignment Posting (Professor)

The professor writes skeleton code for a Java assignment (Calculator.java) that students need to complete and uploads it to the GitHub repository (main branch).

"Alright, this week's assignment is to complete a calculator that finds the sum of two numbers. Variable names must be clear, and there should be no unnecessary code to get a perfect score!"

2. 👨‍💻 Working on the Assignment (Student)

The student brings the professor's repository to their own space (Sync Fork), creates a new branch (homework-1), and solves the problem.

"Hmm, a + bI just need to return that, right? All done! Time to submit it to the professor for review." The student creates a Pull Request (PR) to submit the assignment.

3. 🤖 AI Agent Activation (System)

The moment the student clicks the "Create PR" button, the dormant Spring Boot server detects GitHub's signal (Webhook) and wakes up.

• Step 1 (Analysis): The server extracts only the code changes (Diff) made by the student and passes them to the AI agent.

• Step 2 (Grading):"Hmm, the functionality is correct, but the temp variable is unnecessary. The score is 90 points!" The AI grades rigorously according to its pre-trained persona.

• Step 3 (Save): The grading result (90 points) and feedback content are securely saved in the database (DB).

• Step 4 (Feedback): At the same time, the AI leaves a comment on the student's PR. "The functionality is perfect! However, unnecessary variable declarations can waste memory."

4. 📊 View Results (Student)

The student receives a notification in just 5 seconds after submitting the PR.

"The grading is already done?"

The student accesses a dedicated dashboard (web page) and enters their GitHub ID. The screen displays the score (90 points) of the assignment they just submitted and the AI's feedback, neatly organized in tables and badges.