Letting Agents Run: Decomposition, Context, and Memory
🚧 This chapter is under development. Below is a summary of the core arguments. Full content will be published in a subsequent release.
Volume 1 solved the quality problem for single interactions: write a clear spec, build a solid verification closed-loop control, and the Agent's single-task output can stabilize at a high level. But there is an implicit assumption here: you have been present the entire time. You personally provide the input for every task, monitor the process, collect the result, and trigger the next step. The Agent's speed is not the bottleneck. Your bandwidth is. Three or four tasks a day is your limit, but the Agent can clearly do more.
The bottleneck has shifted from Agent capability to human availability. The direction of the solution is clear: transition from being a real-time conversation partner to being a task designer and acceptor. Hand off the task, go do something else, and come back to collect usable results.
The core structural constraint facing this transition is the context window. When the Agent runs autonomously in an agentic loop, the input and output of each iteration are appended to the context. Once the accumulated tokens exceed effective capacity, output quality does not degrade gradually. It collapses off a cliff. This wall is not a bug. It is an inherent characteristic of LLM architecture. Every approach that lets an Agent execute autonomously must work within this constraint.
Addressing this constraint requires capabilities at three levels. Task decomposition determines the granularity of each execution block, ensuring each block completes before context collapse. Context engineering determines what goes into each block's context window, keeping critical constraints from being drowned out by noise. Cross-session persistence solves the memory reset problem between sessions, letting the next session pick up where the previous one left off.
These three capabilities are not independent toolboxes. They collaborate around the same objective: enabling you to hand a multi-hour task to the Agent, walk away, and come back to an acceptable result. Once this is achieved, the efficiency ceiling is no longer how many tasks you can monitor simultaneously, but how many tasks you can design and accept simultaneously.
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