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Graph orchestration for stateful and controllable AI agents.
Foundation
Compose bounded control flows instead of building one giant autonomous loop
A plain-Python guide to tool use, parallel orchestration, planning, HITL, retry, and reflection—and to the boundary between LLM judgment and deterministic code.
Read article ↗Separate graph state from application state to assign responsibility across workers, Redis, databases, and browsers
A close look at LangGraph's graph.astream(), updates, custom streams, reducers, and checkpoints—and the boundary between an agent runtime and backend architecture.
Read article ↗Perspective
From prescribing every route to designing the environment and boundaries around an agent
After building a multi-agent research system with LangGraph, I am rethinking where explicit graphs belong as models choose more of their own execution paths.
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