# Agentic Ai Projects And Mastery
All Agentic Ai Projects And Mastery notes →1.1 Setting Up the Development Environment
Python project setup, virtual environments, and installing the OpenAI SDK, LangChain, and LangGraph so the rest of the book's code samples run without friction.
1.2 Creating a Tool-Using Agent
Designing an agent from scratch — defining tools, wiring tool calling, building prompt templates, and generating a final response, without a framework in the way.
1.3 Building Agents with LangGraph
Why LangGraph exists, its state-management model, nodes, edges, conditional routing, and how the execution flow maps onto the five-component agent loop.
1.4 Testing and Debugging Agents
Unit testing tools in isolation, mocking LLM calls, debugging a live agent flow, and a troubleshooting guide for the failure scenarios that recur across every agent built in this book.
2. Build an MCP Server
A guided, hands-on build of a Model Context Protocol server exposing a real tool (e.g., an observability query tool) with schema-validated inputs and outputs, deployable and testable end to end.
3. Build an Agent with Memory
Hand-rolling short-term and long-term memory for an agent — SQLite-backed storage for conversation history and investigation history across sessions.
4.1 Building an Operational Knowledge Base
Turning runbooks, playbooks, architecture documents, incident reports, and best practices into a RAG corpus an investigation agent can actually retrieve from.
4.2 Retrieval-Augmented Generation (RAG)
Why RAG exists, document processing and chunking strategy, embeddings, vector database choice, and the retrieval pipeline that feeds relevant context into an agent's prompt.
5. Build a Coding Agent
A guided, hands-on build of a coding agent that reads a repository, plans a change, edits files, and runs tests in a sandboxed loop with a human-review checkpoint before merge.
6. Build a Multi-Agent System
A guided, hands-on build of a multi-agent system applying the supervisor and orchestrator-worker patterns from Part 00 of AI Architecture & System Design to a concrete task, with message-passing and failure-handling code.
7.1 Connecting Agents to Grafana
Wiring an agent's tool layer to Grafana's HTTP API and Prometheus datasource — authentication, the metrics query surface, and the error handling an agent needs when a query fails mid-investigation.
7.2 Building a Log Investigation Tool
Exposing Loki's API and LogQL as an agent tool — time-range filtering, log summarization, and pattern detection so an agent can search logs the way an SRE would.
7.3 Building a Trace Investigation Tool
Exposing Tempo's trace retrieval API as an agent tool — span analysis, latency investigation, and service dependency analysis from a single trace ID.
7.4 Automated Root Cause Analysis
Correlating metrics, logs, and traces into one evidence trail, scoring confidence in a candidate root cause, and generating an incident summary an on-call engineer can trust.
8. Build an Enterprise AI Platform
A guided, hands-on build of a minimal enterprise AI platform slice - gateway, registry, and one deployed agent - wiring together the architecture covered in Part 01 of AI Architecture & System Design into working infrastructure.
9. Production Deployment
Containerizing and deploying an agent through Docker, Kubernetes, and CI/CD — and the one thing that is actually agent-specific: versioning prompts and models as deploy artifacts.
10. Capstone Project
Assembling every Part of this book into one deployable system — architecture, project structure, end-to-end workflow, RCA generation, dashboards, and deployment.