Building Production-Ready Agentic AI: From Tutorial to Real-World Serving Benchmark

Hey devs 👋

If you’ve been building ReAct agents with LangGraph, you’ve probably faced the same question I did:

“I can build a cool agent in a tutorial… but which serving engine should I actually use in production?”

That’s why I connected my two repositories:

• Agentic-AI-Tutorial → Learn how to build a full ReAct agent from scratch

• concurrent-llm-serving → Benchmark vLLM vs SGLang under heavy agent load

Now the two repos are linked: the exact same agent from the tutorial is included as simpleagent/ inside the benchmark repo.

What’s Inside the Agentic AI Tutorial

You start with a clean, production-style LangGraph ReAct Agent that has three nodes:

1. Conversation – Handles multi-turn dialogue

2. Act – Calls real tools (DuckDuckGo Search + Calculator)

3. Summarize – Processes long document context (10k+ tokens)

Everything is explained step-by-step:

• Tool calling

• Structured outputs

• Memory management

• Error handling

Repo → https://github.com/zkzkGamal/Agentic-AI-Tutorial

The Missing Piece: Which Engine Should You Serve It With?

Tutorials usually stop at “run it locally.”

I wanted to go further.

So I took the exact same agent and stress-tested it under 3 concurrent sessions (5 turns each, up to ~25,000 tokens total context) using:

• Model: Qwen3.5-0.8B (single GPU)

• Engines: vLLM vs SGLang

Full benchmark report is here:

README_agent_benchmark.md

High-Level Results

| Metric | vLLM | SGLang | Winner |

|-------------------------------|---------------|----------------|-------------|

| Total Wall Time (3 sessions) | 229.8s | 255.8s | vLLM (-11%) |

| Context Limit Errors | 0 | 2 | vLLM |

| Successful Sessions | 3/3 | 3/3 | Tie |

Node-Level Breakdown (this is where it gets interesting)

• Act Node (Tool Calling) → SGLang wins by 71%

Thanks to RadixAttention prefix caching — perfect for repeated tool calls.

• Summarize Node (Long Context) → vLLM wins

Much more stable when context balloons to 10k+ tokens.

Verdict:

• Use SGLang if your agents do a lot of tool calling in loops.

• Use vLLM if your agents handle heavy RAG or summarization workloads.

How to Use Both Repos Together (The Full Flow)

1. Clone the tutorial and build your agent

   git clone https://github.com/zkzkGamal/Agentic-AI-Tutorial.git

   cd Agentic-AI-Tutorial

1. Move to the serving benchmark repo (now includes simpleagent/)

   git clone https://github.com/zkzkGamal/concurrent-llm-serving.git

   cd concurrent-llm-serving/simpleagent

1. Run the exact same agent with either engine using the provided launch scripts.

Everything is documented — you can literally go from learning the agent pattern to benchmarking production serving in minutes.

Why This Matters

Most agent tutorials leave you with a notebook.

This project gives you the complete pipeline:

• Build the agent ✅

• Understand the serving trade-offs ✅

• Choose the right engine for your workload ✅

• Deploy it at scale ✅

Try It Yourself

• Tutorial repo: Agentic-AI-Tutorial

• Benchmark repo (with integrated simpleagent): concurrent-llm-serving

Would love to hear from you:

• What serving engine are you using for your agents today?

• Have you noticed the same trade-offs between vLLM and SGLang?

• Want me to add more models / workloads / frameworks (CrewAI, AutoGen, etc.)?

Drop your thoughts below 👇

Happy building!

— zkzkGamal