Tool-using systems, multi-agent design, and the engineering that keeps them upright.
An LLM agent with a million-token window still forgets what you told it last week. Memory is not a bigger context; it is a separate system that decides what to write down, how to summarize it, and what to pull back. Here is how t…
RLHF taught a model to answer one question well. Agentic RL asks a harder thing: take fifty actions in a live environment, most of them invisible in the final reward, and still learn which ones mattered. That single change, from …
An agent that can act for eight hours still thinks inside a window that holds a few hundred thousand tokens. The gap between those two numbers is where most agent failures now live, and closing it has become its own engineering d…
A static agent pipeline is a diagram you draw before you know the task. A dynamic workflow is a program the agent writes once it sees the task, then runs deterministically. The difference reshapes how fan-out, verification, and c…
Most agent frameworks treat state as an afterthought, tacking memory onto a chain of LLM calls. LangGraph inverts the design: the graph is the state machine, every node transition is checkpointed, and cycles are first-class citiz…
A single agent burns about 4x the tokens of a chat. A multi-agent system burns about 15x. The whole question of orchestration is whether the extra agents buy you accuracy worth more than that bill, and four named patterns decide …
Fifty-seven percent of organizations now run AI agents in production, yet quality remains the top deployment barrier. This is the field guide for the engineers shipping those systems: frameworks compared, patterns catalogued, fai…
A frontier model can write a function. An agentic runtime can read a codebase, plan a migration, edit forty files, run the tests, fix what broke, and open a pull request. The difference is not intelligence; it is the system that …
The industry frames every LLM feature as an agent. Most production systems that work are pipelines with one or two LLM steps and clear handoffs. Default to a workflow, reach for an agent only when the problem demands it.
The frameworks that demo best are rarely the ones that survive in production. LangGraph wins where it matters by refusing to hide the thing every other framework tries to hide: the control flow.