Could AI agents skip re-planning from scratch each time by reusing a library of plans that already worked?

This explores whether agents can avoid working out the same plan over and over by leaning on stored knowledge and pre-checked plans. One caveat first: the collection has no paper that directly measures a reusable library of validated plans. It does say a lot about the general idea of moving planning work out of the model and into structures around it, and that evidence points one way. The strongest framing is Where does agent reliability actually come from?. It argues that reliable agents don't come from bigger models. They come from offloading memory, reusable skills and interaction protocols into a harness layer, so the model no longer has to 'solve the same problems repeatedly.' In practice, that repeated solving is what replanning overhead is.

The most concrete evidence that stored organizational knowledge cuts planning effort comes from software. In Can explicit behavior maps help weaker planners compete with stronger models?, the researchers gave a planner a map linking what a system does to where that behavior lives in the code. The planner used 8–13% fewer tokens, won 10–19 points more often, and a weaker model with the map matched stronger models at finding the right code. The surprising part is that good organizational memory can stand in for raw model capability. The planner spends less effort because it no longer has to rediscover how the system is organized. Can agent harnesses be automatically optimized across many environments? tells a similar story at a larger scale: automatically tuning the harness found mechanisms (compacting context, handing off reading, handling observations) that cut token traffic by roughly 45–49% with no loss in performance.

Memory alone doesn't remove replanning. It depends on how the memory is structured. Can agents compress their own memory without losing critical details? has agents compress their own history into three stores: past episodes, current work and tool knowledge. This both saves tokens and gives the agent a clean point to pause and rethink its strategy, which makes replanning deliberate instead of constant. Can agents reconstruct memory on demand instead of retrieving it? goes further. Rather than retrieving a fixed bundle of facts and then reasoning over it, the agent explores its memory step by step and drops paths that the evidence rules out. That improved reasoning by up to 23% and lowered cost. Should agent memory live inside the model backbone? takes the opposite approach and builds memory into the model itself, arguing that external memory and the model can drift apart when they are trained separately.

The 'validated' part of the question matters more than it first appears. Why do multi-agent systems fail to coordinate at scale? found that agents accept information from neighboring agents without checking it, so errors spread across the network. Unvalidated shared plans don't just fail to save effort. They can multiply mistakes. One way to validate plans is to keep a human involved: Can users steer AI agents better by revising plans mid-execution? lets users edit and assign plan steps during execution, and users found this easier to steer than starting over through chat.

There is also an economic reason for all this. How does test-time scaling work at the agent level? reports that about 80% of multi-agent performance differences come down to how many tokens are spent, so any replanning you avoid shows up directly as lower cost. Can small language models handle most agent tasks? adds an implication: once a plan is settled and well defined, the repetitive steps can go to small models at 10–30× lower cost, with large models saved for actual replanning. The takeaway is that pre-checked plans and organized memory don't just make an agent faster. They change which model you need to run it.