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 …
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 …
One workflow, 150,000 tokens down to 2,000. A subagent that burns fifty thousand tokens and returns fifteen hundred. A hundred installed skills that cost two thousand tokens until one is needed. Four separate advances, four separ…
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.