10 articles
Autonomy is great until the agent deletes the wrong file or emails the wrong list. Here is how DeepAgents pauses on sensitive tool calls and waits for a human, in TypeScript.
Skills give an agent deep, reusable capability without paying for it on every turn. Here is how DeepAgents loads them in layers, and how to write ones that get picked.
DeepAgents treats long-term memory as files the agent reads and writes, with backends deciding where they live. Here is how scoping, episodic memory, and consolidation actually work.
Subagents are the main lever for stopping a long task from drowning in its own context. Here is how DeepAgents implements them in TypeScript, and the mistakes that make them useless.
A tour of how DeepAgents assembles, compresses, and isolates context, with the TypeScript config for each layer. The framework does a lot automatically; this is what is happening and where you steer it.
What state, checkpoints, and threads actually are in LangGraph, how to pick a checkpointer, and how persistence powers memory, resume, and human-in-the-loop.
Deep Agents bakes planning, context management, subagents, and human approval into the agent loop. Here is why it exists, every main feature, and a production-grade customer support agent built with it.
You cannot improve an agent you cannot measure. A deep, practical guide to evaluating agents with LangSmith: tracing, datasets, evaluators, experiments, and what makes agent evaluation different.
An agent with no tools is just a chatbot. Here is how tool calling really works, what makes a tool the model can use reliably, and how MCP lets you plug in tools you did not write.
Agents, explained without the hype: what they are, when one is enough, when you need several, and how to build a working multi-agent system with LangChain.