AI DevOps Korea
Aidevops.kr organizes LLMOps, RAG, agents, evaluation, observability, and cost-performance tuning for teams running AI in production.
A practical blueprint for a RAG-based AI stock investment Agent. Covers product goals, user scenarios, system boundaries, core components, and end-to-end architecture for a research and paper-trading workflow.
A practical guide to building the RAG data layer for an AI stock investment Agent. Covers price data, news, SEC filings, earnings transcripts, normalization, chunking, metadata, and freshness-aware retrieval.
A practical design for the workflow of an AI stock investment Agent. Covers routing, query parsing, screening, retrieval analysis, quantitative analysis, risk evaluation, and final report composition.
Strong stock analysis is not enough to build a real investment Agent. This post explains position sizing, sector concentration limits, event risk, backtesting design, and paper-trading workflows.
A practical implementation blueprint for a RAG-based stock investment Agent using FastAPI, PostgreSQL, pgvector, Redis, async workers, and domain-separated service modules.
A practical operations guide for a stock investment Agent. Covers paper-trading workflow, human approval, monitoring, alerts, audit logs, failure handling, and the guardrails needed before any real execution.