English series
Harness Engineering
English editions of Guozhen AI articles. The text is localized for global readers while the original diagrams, screenshots, and code examples remain aligned with the Chinese source.
5 Checkpoints and Memory: Help Long Agent Tasks Recover the Main Thread
Checkpoints and layered memory let long-running AI agents compress progress, preserve useful facts, drop noise, and re-plan without losing the original goal.
Read lesson4 Planner and Executor: Split Long Agent Tasks into Controllable Loops
Planner and Executor separation lets an AI agent break long work into clear tasks, run one step at a time, observe reality, update state, and re-plan.
Read lesson3 State Engineering: Preserve Agent Progress with Structured State
State Engineering keeps an AI agent aware of progress, decisions, artifacts, blockers, and the next action without stuffing the whole transcript into context.
Read lesson2 Goal Manager: Pin the Agent's Main Objective Outside the Model
A Goal Manager keeps the agent's main objective outside the conversation transcript, so long-running tasks can stay aligned even after many tool calls.
Read lesson1 What Is Harness Engineering: Keep Agents on Track Without Relying on Memory
Harness Engineering is the system layer that keeps an AI agent aligned with its goal, state, plan, checkpoints, and memory across long-running work.
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