AI Dev Reads
AI engineering posts picked from company and developer tech blogs such as Toss, LINE, Anthropic and OpenAI, each with a short summary. Tap to read the original.
Last searched Oct 19, 7:44 PM KST
Zero's BlogAdded Oct 19
What exactly is RAG?
Using a shopping-mall support bot as the example, it explains RAG, fetching only the relevant chunks for each question, with a LangChain code walkthrough. Citing Anthropic's Contextual Retrieval, it notes retrieval failures fell 35% with chunk context, 49% with keyword search added and 67% with reranking, and suggests just putting everything in the prompt when docs are under 200K tokens.
AgentsModels & evals
Does Claude really remember your name? LLMs and context
Each API call is independent, so the model remembers nothing between calls; chat apps just resend the whole conversation, which makes it look like memory. Send "my name is Youngsu" along and it uses the name; drop it and it says you never told it. The same applies to Claude Code's CLAUDE.md and memory files, which only help once they're loaded into context.
Models & evalsAgents
What exactly are LangChain and LangGraph?
LangChain supplies the parts (model wiring, prompts, tools, RAG), while LangGraph is an execution engine that controls flow with state, nodes and edges. They sit at different layers rather than replacing each other. Use the model API directly for single calls, LangChain for tool-using agents, and LangGraph when you need human approval, branching or pause-and-resume.
Agents
LangChainAdded Oct 19
The Most Common Questions About Agent Evals, Answered
Where to start with agent evals: offline vs online evals, evaluator types and LLM judges, benchmarks, multi-turn evals, and how to keep the cost down.
AgentsModels & evals
Anthropic EngineeringAdded Oct 19
Redesigning source control for always-on agents (Anthropic)
As daily merges grew from about 2,500 in May to over 30,000 by September, Anthropic replaced git for its monorepo with Scion, an in-house system that keeps commits, merges and history on the server and fetches only needed files. It uses about 200x less CPU and memory than the old git mirrors and checkouts dropped from minutes to seconds. Anthropic says it isn't a general-purpose git replacement.
Infra & servingAgents
Open SWE: An Open-Source Framework for Internal Coding Agents
Built on Deep Agents and LangGraph, Open SWE gives teams the core building blocks for an internal coding agent, so they don't have to start from scratch to wire one into their own repos and tools.
Coding toolsAgents
Hugging FaceAdded Oct 19
Run fast, private agents on-device with LFM2.5-24B-A2B
Liquid AI's 24B-parameter reasoning model activates only 2B parameters per token, so it runs fast on your own hardware: 184 tokens/s on an Apple M5 Max and 114 tokens/s on a Ryzen AI Max+ 395. The open weights are on Hugging Face.
Models & evalsAgents
Latent SpaceAdded Oct 19
How Anthropic's Dynamic Workflows Coordinate Claude Agents
A write-up of Anthropic's AI Engineer New York talk. It borrows lessons from Unix and Linux to show how agents can split up bigger projects without overwhelming the developers who oversee them. The talk video is included.
Agents
LY CorporationAdded Oct 19
Trusting LLM-written SQL enough to run it: building an A2A conversational BI app (LY Corp)
LY's game platform team built a chat BI app that goes from question to SQL to chart, with A2A agents splitting the roles. To trust generated SQL they added ontology-based RAG for metric definitions, a separate pre-execution guardrail for row- and column-level permissions, and feedback that explains violations.
AgentsSecurity
카카오 테크Added Oct 19
Context design lessons from building a data-analysis agent (Kakao)
Kakao's agent finds the right data, writes and runs SQL, and explains the result. The post breaks context into system prompt, tool specs and message history, and shows with example prompts how, as models improved, step-by-step workflow rules moved out of the system prompt and into per-tool usage conditions.
AgentsCase studies- 네이버 D2
네이버 D2Added Oct 19
Agents from first principles: workflows, harnesses, context/memory, MCP/A2A (NAVER D2)
A popular NAVER internal tech meetup talk (video). It untangles terms teams use differently, such as workflow, harness, context vs memory, and MCP vs A2A, by going back to the Anthropic and OpenAI write-ups and the protocol specs.
Agents 
How much to hand to the LLM: automating ad analytics reports with AI agents (LY Corp)
LINE Ads' data team automated a daily revenue-change report sent to Slack and email. Code handles calculations, candidate selection and consistency checks; the LLM interprets verified facts into prose; and a tool-calling agent explores causes only within allowed analysis dimensions.
AgentsCase studies