# LinkedIn Post Draft — CrewAI Course

## Option 1: Learning in Public

I'm currently taking Ed Donner's "AI Engineer Agentic Track" course on Udemy — a 6-week deep dive into building AI agents with OpenAI Agents SDK, CrewAI, LangGraph, AutoGen, and MCP.

Week 1 just wrapped. Here's what I've learned so far:

**The 5 LLM Workflow Design Patterns that matter:**
1. **Prompt Chaining** — break complex tasks into sequential steps
2. **Routing** — classify inputs and route to specialized handlers
3. **Parallelization** — run independent tasks simultaneously
4. **Orchestrator-Worker** — a manager agent delegates to specialists
5. **Evaluator-Optimizer** — generate, evaluate, improve in a loop

**Key insight:** An agent is just an LLM + tools + a loop. That's it. No framework needed to start.

**What I built this week:**
- Multi-model competition: asked the same question to 6 different LLMs (all free via OpenRouter), had a judge model rank the responses
- Agentic AI solution for legal document review: 7 specialized agents working together to solve the "reactive firefighting" problem in large-scale document review

The exercise that hit hardest: "Showing beats telling every time." Build the prototype first, then make the argument.

Next up: Week 2 — OpenAI Agents SDK.

If you're building with AI agents, I'd love to connect and share notes.

#AI #Agents #LLM #CrewAI #OpenAI #AgenticAI #LearningInPublic

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## Option 2: Shorter / More Personal

Just finished Week 1 of Ed Donner's AI Engineer Agentic Track course.

Biggest takeaway: the difference between "AI that predicts" and "AI that acts" is agency.

An agent = LLM + tools + a loop. That's the whole thing.

Built a multi-model competition this week (6 free LLMs, one judge) and designed a 7-agent system for legal document review.

The pattern I keep coming back to: "Showing beats telling." Build the prototype. Then make the argument.

Week 2 starts Monday. If you're on the same journey, let's connect.

#AI #Agents #LLM #LearningInPublic
