Designing Real-World AI Agents Workshop

A hands-on workshop 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.

Built as a lightweight companion to the Agentic AI Engineering Course, which covers 34 lessons and three end-to-end portfolio projects. This workshop distills the core agentic patterns into a ~2-hour hands-on build.

Link to the slides here.

What You'll Build

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 + guideline → generate post → [review → edit] × N → post.md → generate image

Both servers expose tools, resources, and prompts via the Model Context Protocol, letting any MCP-compatible harness orchestrate the workflow.

Patterns and concepts you'll learn:

• Tool-use agents — letting the LLM decide which tools to call and when
• Evaluator-optimizer loop — generate, review, edit in cycles
• Grounded search — Gemini with Google Search grounding for factual research
• Structured LLM output — Pydantic schemas for type-safe model responses
• MCP server design — registering tools, resources, and prompts with FastMCP
• LLM-as-judge evaluation — automated quality scoring with Opik

Example: End-to-End Workflow

Here's a real run through the full pipeline — from a topic seed to a published-ready LinkedIn post with an AI-generated image.

Final output

Phil Tobaloo AI Engineer | I ship AI products and teach you about the process. We planned 12 AI agents and shipped 1. It worked better. Sounds crazy, right? But it's a common story. A client built an AI marketing chatbot. Their initial design had dozens of agents: orchestrator, validators, spam prevention. It failed. A single agent with tools won. Tasks were tightly coupled. One brain maintained context. Tools were still specialized. This is the core mistake. People jump to complex multi-agent setups too fast. Think AI system design as a spectrum: Workflows: You control steps. Single Agent + Tools: Model decides flow. Multi-Agent: Multiple decision-makers. ... A single agent works for most cases. But it has limits. Too many tools? You hit "context rot." Past ~10-20 tools, LLMs degrade at tool selection. They get overwhelmed. Information gets lost in the middle. So, when do you actually need multi-agent? ... The simplest system that reliably solves the problem is always the best system. Don't overengineer your AI agents. Build simple first. What's the most complex agent architecture you've simplified? Tell me below. Read the full post

# Research Topic: AI Agent Architecture — When Less Is More

## Key Questions
1. Why do single-agent architectures with smart tools outperform multi-agent systems?
2. What are the only legitimate reasons to adopt a multi-agent architecture?

## References
- Stop Over

... [View full README on GitHub](https://github.com/iusztinpaul/designing-real-world-ai-agents-workshop#readme)