Mahmoud Malek
AI Engineering Student | LangChain Enthusiast | Azure AI Foundry | Scout Leader
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I'm Mahmoud Malek, an AI engineer with a bachelor's degree in Computer Science, graduated with excellence. My final-year internship focused on developing a production-grade multi-agent educational platform using LangGraph, LangChain, FastAPI, and Azure AI Foundry. By now, I've spent over 50 hours reading the LangChain documentation, which probably tells you something about how deeply I like to understand the tools I use.
I've been an active participant in AICO events across Tunisia this year, staying closely engaged with the local AI community and keeping up with developments in agentic AI.
Outside of engineering, I'm a scout with twelve years of experience and currently serve on the International Relations Committee. I have significant experience in public speaking, including serving as an awareness ambassador for a joint UNICEF–Tunisian Scouts initiative, helping educate communities about COVID-19 and the practical steps they could take to protect themselves. I also serve as a Scout Go Solar ambassador, promoting environmental awareness and sustainable practices through scouting initiatives.
I enjoy building systems, sharing what I learn, and helping others grow—whether that's through AI or through scouting.
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From Prompt to Production: Building Real Agent Systems with LangChain, LangGraph & LangSmith
Most AI demos work perfectly right up until they leave the notebook.
This workshop is about closing that gap: transforming a simple prompt-and-response prototype into a system you can actually trust in production.
You'll discover why LangChain, LangGraph, and LangSmith aren't three disconnected tools, but three layers of the same challenge.
LangChain provides the building blocks: models, prompts, tools, retrieval, and integrations.
LangGraph provides orchestration: branching workflows, loops, parallel execution, state management, and failure recovery.
LangSmith provides visibility: tracing, debugging, evaluation, and observability so you can understand what your agents are really doing and improve them with confidence.
Drawing on real architectural patterns from a multi-agent educational platform that I designed, built, and defended as my final-year engineering project, including fan-out/fan-in subgraphs and a dual-memory architecture powered by Redis, we'll move beyond tutorials and explore how modern agentic systems are actually built.
In this workshop, you'll learn:
The distinct roles of LangChain, LangGraph, and LangSmith, where their responsibilities begin and end, and why understanding those boundaries matters.
How to use each framework independently and how to combine them into a cohesive production-ready stack.
How to design agent workflows as graphs rather than linear chains, and more importantly, how to recognize when graph-based orchestration is genuinely necessary.
How to implement short-term and long-term memory, along with the architectural trade-offs between persistence, context, latency, and cost.
How to build fan-out/fan-in patterns in LangGraph to execute multiple agent branches in parallel and reduce overall latency.
How to trace agent decisions, debug tool calls, and evaluate system behavior with LangSmith, replacing guesswork with observability.
How to approach testing and evaluation for agentic systems, where outputs are probabilistic rather than deterministic.
The practical challenges involved in moving from a working prototype to a deployable, observable, and maintainable production system.
By the end of this session, you'll understand not only how to build agents, but how to architect, debug, evaluate, and operate them in the real world.
Because building an AI demo is easy. Building an AI system people can rely on is where the real engineering begins.
Build, Orchestrate, Observe: Making LangChain, LangGraph & LangSmith Work as One System
Most AI projects start with a simple prompt and response. The real challenge begins when you need that prototype to handle complex workflows, remember information, use tools, and be reliable enough for real users.
In this session, we'll explore how LangChain, LangGraph, and LangSmith work together to help you build production-ready AI agents.
Using examples from a real multi-agent educational platform built as my final-year engineering project, we'll cover how to design agent workflows, manage memory, run tasks in parallel, and debug agent behavior with confidence.
You'll learn:
What LangChain, LangGraph, and LangSmith are, and when to use each one.
How these frameworks work together to build complete AI applications.
When a simple chain is enough and when you should use a graph-based workflow.
How to give agents short-term and long-term memory.
How to build parallel agent workflows with fan-out/fan-in patterns.
How to trace, debug, and evaluate agent behavior using LangSmith.
The practical challenges of moving from a demo to a production-ready system.
By the end of the session
You'll understand how modern AI agents are built, orchestrated, monitored, and improved—and you'll have a clear roadmap for taking your own projects from prototype to production.
Mahmoud Malek
AI Engineering Student | LangChain Enthusiast | Azure AI Foundry | Scout Leader
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