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agentic-ai-engineering
Agentic AI Engineering is a production-grade engineering resource for building modern agentic AI systems with LangChain, LangGraph, RAG, MCP, local models, and deployable Python services.
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Agentic AI Engineering
Agentic AI Engineering is a production-grade engineering resource for building modern agentic AI systems with LangChain, LangGraph, RAG, MCP, local models, and deployable Python services.
The repository leads toward the architecture implemented in Chapter 5: a multi-node LangGraph assistant connected to a standalone MCP server, with intent routing, tool execution, response summarization, email tooling, math tools, automated tests, and GitHub Actions CI. Earlier chapters build the required layers underneath it: provider abstraction, LCEL orchestration, vector retrieval, memory, ReAct agents, router graphs, sequential workflows, multi-agent collaboration, and human-in-the-loop control.
This is not a beginner chatbot walkthrough. It is a structured engineering path for developers building systems that need state, tools, routing, retrieval, observability, modularity, and model-provider flexibility.
What You'll Build
Multi-node LangGraph assistant with router, execution, summarization, and conversation nodes.
MCP tool server exposing isolated math and email tools over a decoupled server boundary.
Tool-using ReAct workflows that call external capabilities through typed tool contracts.
RAG pipelines using vector stores, embeddings, retrieval chains, and local document context.
Provider-flexible LLM interfaces across OpenAI, Gemini, and Ollama.
Agent routing systems for sequential, router-based, ReAct, and multi-agent workflows.
Human-in-the-loop execution paths for safer agent behavior in production-style flows.
Tested agent components with pytest coverage for Chapter 5 node behavior and tool contracts.
CI-backed agent validation through GitHub Actions for repeatable checks on graph and MCP behavior.
Direct integration with OpenAI, Gemini, and Ollama. Covers provider calls, streaming responses, prompt structure, and model-facing interfaces used later by orchestration and agent layers.
Builds LCEL pipelines, sequential chains, router chains, prompt templates, output handling, and tool binding. This chapter establishes the composition patterns used by larger agent systems.
Implements a decoupled agent runtime with a LangGraph assistant and MCP server. The assistant routes requests across specialized nodes, invokes MCP tools, summarizes tool outputs, and validates behavior with automated tests and CI.
