AI Agent Development Journey — Day 4 · Make $1k-$10k in 30 days

AI Agent Development Journey — Day 4

Understanding LLM Workflows, LangGraph Execution Model & Hands-on Practice

Day 4 of my AI Agent Development journey took me deeper into how LLM-based systems are structured — and how real AI workflows are designed using LangGraph.

Along with the theory, I also created a hands-on notebook, where I built simple conditional, sequential, and parallel workflows to strengthen my understanding.

🔹 LLM Workflow Types I Explored Today

1️⃣ Prompt Chaining — Sequential prompting where each output feeds the next step.

2️⃣ Routing Chaining — Choosing which LLM or chain solves which problem.

3️⃣ Parallelization Chaining — Breaking a task into sub-tasks, running them together, then combining results.

4️⃣ Orchestration Chaining — An LLM deciding which sub-model is best for each step.

5️⃣ Evaluation Chaining — One model answers, another evaluates; if not satisfied → regenerate.

🔹 Core Graph Concepts in LangGraph

🔹 Nodes — Define what work is performed.

🔹 Edges — Define how the workflow transitions.

🔹 State — A shared memory (typed dict / Pydantic model) flowing through the graph.

🔹 Reducers — Define how updates to the shared state are merged.

🔹 LangGraph Execution Model

Today I learned how LangGraph manages:

Input handling
Node execution
State updates
Conditional routing
Parallel flows
Cyclic workflows

And I practiced implementing all of this by writing a custom notebook that includes:✔ Simple conditional workflow✔ Sequential workflow✔ Parallel workflow

This practical coding session helped me understand how real AI agents operate under the hood.

👉 Here is the notebook — check out the code and feel free to share your feedback:

https://github.com/umiii-786/AI-Agent-Learning-Notebooks-/blob/main/day2_of_AI_agent_making_simple_conditional_workflow.ipynb

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