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Hands-on workshop: Build a multi-agent AI system from scratch — Deep Research Agent + Writing Workflow served as MCP servers. Includes code, slides, and video
| Date | Stars |
|---|---|
| 2026-07-31 | 482 |
| 2026-08-05 | 484 |
| 2026-08-06 | 484 |
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# Build Your Own Deep Research Agent + Technical Writer Multi-Agent System A hands-on workshop, presented at [AI Engineering Conference Europe](https://www.ai.engineer/europe), building a multi-agent AI system with two MCP servers: a **Deep Research Agent** and a **LinkedIn Writing Workflow**. Both connected to a harness like Claude Code or Cursor. 🎬 Full workshop available on [YouTube](https://www.youtube.com/watch?v=mYSRn6PC1mc) ↓ <a href="https://www.youtube.com/watch?v=mYSRn6PC1mc"> <img src="https://img.youtube.com/vi/mYSRn6PC1mc/maxresdefault.jpg" alt="Watch the video" style="width:100%; max-width:600px;"> </a> 📑 Slides [here](https://drive.google.com/file/d/1RWdS5VQYjz7a9y7NzHhAnyhGtxi6e0vt/view?usp=sharing). ---- ## Whenever You're Ready, Here's How to Go Deeper <a href="https://academy.towardsai.net/courses/agent-engineering?utm_source=github&utm_medium=aieng&utm_campaign=2026_aieng_workshop&utm_id=researchwriter"><img src="media/course_clip.gif" alt="Agentic AI Engineering Course" width="800"/></a> This workshop is a 2–4 hour taste. If you want to go from zero to shipping production-grade AI agents, check out our [**Agentic AI Engineering Course**](https://academy.towardsai.net/courses/agent-engineering?utm_source=github&utm_medium=aieng&utm_campaign=2026_aieng_workshop&utm_id=researchwriter), built with Towards AI. **34 lessons. Three end-to-end portfolio projects. A certificate. And a Discord community with direct access to industry experts and us.** Rated 5/5 by 300+ students. The first 6 lessons are free: [**Start here →**](https://academy.towardsai.net/courses/agent-engineering?utm_source=github&utm_medium=aieng&utm_campaign=2026_aieng_workshop&utm_id=researchwriter) ---- ## How to Use This Repo Three ways to use this repo. Pick the mode that fits the time you have. Or work through all three in order, since each builds on the last: 1. **Watch the workshop and see the patterns end-to-end. Watch in ~2 hr.** Start with the [2-hour YouTube workshop](https://www.youtube.com/watch?v=mYSRn6PC1mc) and the [slides](https://drive.google.com/file/d/1RWdS5VQYjz7a9y7NzHhAnyhGtxi6e0vt/view?usp=sharing) above. You'll come away with a mental model of the full multi-agent system: tool-use agents, evaluator-optimizer loops, grounded search, structured LLM output, and MCP-server design. 2. **Run the finished code. See it produce real artifacts. Run in ~30 min.** Watch the system generate a research brief, draft a LinkedIn post through an evaluator-optimizer loop, and score itself with an LLM-as-judge. Follow the [Getting Started](#getting-started) and [Running the Code](#running-the-code) sections to install the project and run the MCP servers, skills, and evaluation pipeline. 3. **Implement it yourself with agentic coding. Build a 1:1 replica from scratch in ~2–4 hr.** Open [`implement_yourself/`](implement_yourself/), a stripped-down skeleton prepared with 25 pre-groomed tickets and a custom `/implement` Claude Code skill that orchestrates SWE and Tester agents in a loop, ticket by ticket, until the directory matches `src/`. See [`implement_yourself/README.md`](implement_yourself/README.md) for the kickoff guide. > **No cheating, by design.** `implement_yourself/` is a self-contained project. Open your harness (Claude Code, Cursor, …) **directly in that folder** (not at the repo root) so its working directory is scoped to the skeleton. The agents can't see the reference implementation in `../src/`, can't grep it, can't read its files. You get a real build, not a copy-paste. ## What You'll Build Today **Deep Research Agent** — An MCP server that runs deep research using Gemini with Google Search grounding and native YouTube video analysis: ``` user topic → [deep_research] × N → analyze_youtube_video (if URLs) → [deep_research gap-fill] → compile_research → research.md ``` **LinkedIn Writing Workflow** — An MCP server that generates LinkedIn posts with an evaluator-optimizer loop: ``` research.md + g
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6dd553c57af43bfc, topic:ai-agent, topic:multi-agent-systems, name:ai agents