Production Deployment
Purpose
[stub: production-deployment]
Metadata
| Author | Amit Singh |
| Scope | agentic-ai-projects-and-mastery |
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Linked from 2 notes
12. Rollback Strategies
Reverting a bad prompt, model, or tool-schema change quickly — versioned prompt and model artifacts as first-class deploy units, canary and shadow rollout patterns for agent changes, and the rollback trigger thresholds tied to online-evaluation regressions.
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.