Name: Build Your First AI Agent in 60 Minutes
Start: 2026-08-09T14:30:00.000Z
End: 2026-08-09T15:29:00.000Z

What You Will Build

By the end of this session, you will have a Research & Write Crew — a multi-agent system that mirrors how real engineering teams divide work.

Agent	Role	What It Does
Researcher	Information gatherer	Searches the web, collects facts, and returns structured findings on any topic
Writer	Content producer	Turns raw research into a clear, blog-style summary
Reviewer	Quality gate	Checks accuracy, flags weak claims, and suggests improvements before output ships

This is not a toy demo. It is the same research → build → review loop you will use in internships, hackathons, and production codebases — except here, each role is an AI agent you configured yourself.

You fork a template, run it locally, and push the result to GitHub. In 60 minutes, you go from "I have heard of AI agents" to "I built one."

Why This Matters for Engineering Students

Every engineering student learns loops, functions, and data structures. Very few leave college having shipped something that uses AI in a real workflow.

That gap shows up immediately in interviews:

"Tell me about a project where you used an API."
"Have you worked with LLMs or agent frameworks?"
"How do you break a complex task into smaller parts?"

This workshop gives you a direct answer to all three — in one afternoon.

What changes for your career

You stop being a tutorial consumer. Forking, running, and modifying a repo is how engineers actually work. This session trains that muscle, not just the syntax.
You speak the language companies are hiring for. Agentic AI, multi-step workflows, and API integration are showing up in job descriptions across backend, full-stack, and ML-adjacent roles — even at the intern level.
You get a project worth showing. A GitHub repo with a working multi-agent crew is more convincing than another "To-Do app in React" on a resume that looks like everyone else's.
You build confidence before the hard stuff. You do not need to understand transformers or embeddings today. You need to understand: agent = role + goal + tool. That mental model unlocks everything that comes next.

The industry connection

Companies do not hire students for what they memorized. They hire for what they can demonstrate.

When you walk into an interview and say "I built a three-agent CrewAI system that researches a topic, writes a draft, and reviews its own output — here is the repo", you are not listing a skill. You are proving you can:

Integrate third-party APIs under time pressure
Design a system with clear separation of concerns
Ship something end-to-end, not just write code in a notebook

That is the difference between a student who applied and a student who gets called back.

Skills You Will Carry Into Industry

Skill	Where You Use It
API integration	Every modern backend — payments, auth, LLMs, cloud services
Multi-agent design	Automation pipelines, CI/CD, microservices, AI product features
Prompt engineering	Getting reliable output from LLMs — a skill hiring managers now explicitly ask for
Git workflow	Fork, clone, commit, push — non-negotiable in any engineering team
Debugging live systems	When your agent returns garbage, you learn to trace the problem — not restart the tutorial

These are not "AI buzzwords." They are the same building blocks used in production systems at startups and large tech companies today.

Prerequisites

You do not need an ML background. You need a laptop and willingness to type along.

Requirement	Notes
Python installed	Basic familiarity is enough — if you have written a function and imported a library, you are ready
OpenAI API key	Free tier works for the workshop; we walk through setup live
GitHub account	Used to fork the template and push your finished agent

Stuck on setup? That is normal. Mentors will pair you with someone who got it working — asking for help early is exactly what engineers do on day one of any job.

Post-Workshop: Turn This Into Career Momentum

Do not let this sit as a folder on your desktop. The students who move fastest treat every workshop as a launchpad.

Push your agent to GitHub — Make the repo public. Add a README that explains what each agent does in two sentences. Recruiters click repos; give them something to see.
Write a 2-line LinkedIn post — "I built my first AI agent today using CrewAI. Three agents: one researches, one writes, one reviews. Link to repo below." Visibility compounds. Post the same week you build.
Join Challenge 1 (AI Resume Builder) — The skills from this workshop — API calls, structured output, agent logic — are exactly what Challenge 1 requires. You will not be starting from zero.
Add it to your resume — One bullet under Projects: "Built a multi-agent research crew with CrewAI (Python, OpenAI API) — automated web research, content drafting, and quality review."

The workshop ends in 60 minutes. Your career leverage from it lasts much longer — if you ship, share, and keep building.

Mentor Briefing (Internal)

Do not explain transformers or embeddings. Students don't need it yet.
Focus on: "agent = role + goal + tool" — repeat this until it sticks.
If someone gets stuck on API keys, pair them with a neighbor who got it working.
The goal is confidence, not expertise — one working agent beats one perfect explanation.
Remind students: this project goes on GitHub and LinkedIn the same day. Help them feel proud of what they built.

Build Your First AI Agent in 60 Minutes

Event Timeline

What is an AI agent? Real examples

Fork template + environment setup

Build the Researcher agent

Build the Writer + Reviewer agents

Run the full crew on a live topic

Q&A + how to customize it

About This Event