Building Agents with LangGraph
Purpose
[stub: building-agents-with-langgraph]
Metadata
| Author | Amit Singh |
| Scope | agentic-ai-projects-and-mastery |
Local graph
Linked from 2 notes
3. LangGraph
Covers LangGraph's graph-based state machine model for agent orchestration — nodes, edges, and conditional routing — and why that model, not a simple DAG, is what makes cycles, checkpointing, and human-in-the-loop interrupts first-class instead of bolted on.
Agentic AI: Projects & Engineering Mastery
A book-shaped table of contents for Agentic AI: Projects & Engineering Mastery: hands-on practitioner builds, Principal/Staff-level technical leadership, and the lookup appendices and vendor/framework reference notes for the whole series. Book 6 of the AI Systems Engineering series.
Related notes
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.
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.