​Overview

​A documentation agent spots a change, calls a tool, and receives conflicting information. Does it publish, retry, or ask someone to review?

​In this workshop, Anas Riad guides you through those decisions using a production-ready documentation agent. Follow how LangGraph preserves progress, MCP connects tools, and validation, recovery, and human approval keep updates controlled.

​Diagnose routing errors and invalid responses independently in focused Colab exercises, then review fixes together. Explore AWS deployment and monitoring through a prepared demonstration, with RAG and testing supporting the system.

​Leave understanding how these pieces work together to keep an agent reliable when something goes wrong.

​
This live event is for you because

• ​You are an ML/AI engineer or LLM engineer developing applications that need reliable state management, tool use, and controlled execution.

• ​You understand basic LLM concepts and want to connect them within an agent system.

• ​You want to diagnose routing failures, invalid tool outputs, uncertain results, and retrieval issues.

• ​You want a practical understanding of permission boundaries, human oversight, and operational visibility.

​

​What you will learn and how you can apply it

• ​Trace LangGraph state, conditional routing, checkpoints, and recovery paths to understand and correct workflow behaviour.

• ​Inspect MCP tool contracts and maintain clear boundaries between approved source reads and documentation writes.

• ​Validate structured outputs with Pydantic and handle invalid responses before they affect later decisions.

• ​Route uncertain or conflicting updates to human approval and verify selected behaviours with focused tests.

• ​Examine how retrieval over approved documentation supports answers with source references.

• ​Follow the AWS deployment path and use structured logs to investigate successful and failed runs.

​

​What you will build

• ​Complete three Colab exercises to fix routing, invalid tool outputs, and human approval paths.

• ​Explore a prepared documentation agent that detects source changes and proposes updates.

• ​Examine read-only source access, restricted writes, and human review of uncertain changes.

• ​Inspect source-backed RAG answers and diagnose retrieval issues.

• ​Follow an AWS deployment demonstration and inspect application logs without deploying individually.

​

​Prerequisites

• ​Basic understanding of LLM concepts, applications, and tool calling.

• ​Relevant Python, ML/AI, or LLM engineering experience, with enough Python proficiency to read, modify, and run short code examples.

• ​Familiarity with APIs and Git.

• ​No prior LangGraph or MCP experience is required.

• ​Basic Docker or AWS knowledge is helpful but not required.

​

​What you will walk away with

• ​A practical understanding of stateful workflows, MCP tool integration, validation, recovery, and human oversight.

• ​Experience diagnosing and fixing selected project failures through focused exercises.

• ​A reference architecture you can adapt to documentation, support, research, and internal knowledge workflows.

• ​An understanding of the application's AWS deployment path and how structured logs support troubleshooting.

• ​Access to selected workshop resources, including project code, an architecture overview, and references for LangGraph, MCP, and AWS.