Connecting Agents to Grafana
Purpose
[stub: connecting-agents-to-grafana]
Metadata
| Author | Amit Singh |
| Scope | agentic-ai-projects-and-mastery |
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Linked from 3 notes
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.
What is Azure SRE Agent (Microsoft)
Microsoft's AI agent embedded in Azure for autonomous incident response — acknowledges alerts from PagerDuty/ServiceNow/Azure Monitor, investigates via Azure Monitor/App Insights/Kusto, executes configurable Incident Response Plans with tunable autonomy, and learns across incidents via Session Insights.
What is Harness Engineering
The discipline of designing everything that wraps a raw LLM into a reliable agent — tool contracts, system prompt architecture, context/memory management, permission gates, and feedback loops. Distinct from prompt engineering and model training.
Related notes
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.
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.