# The winning setup used less than half the tokens and finished more tasks

IBM Research compared two ways of handing an agent what it has already learned.

Agentic Context Engineering injects the whole playbook every time.

ALTK-Evolve retrieves only the guidelines that fit the task and the model's capacity.

On DeepSeek-V3.2 the selective version reached 89.3% task completion at roughly 263K tokens per task, against 80.4% at roughly 634K for the full playbook. https://huggingface.co/blog/ibm-research/altk-evolve-sldd

Both agents had access to the same lessons.

The difference was that one of them sent all of them.

The instinct almost everyone has, when an agent underperforms, is to hand it more context.

That instinct is why context windows keep growing while reliability does not.

A larger window does not make a model read better. It makes it read more.

The real shift is treating context as a budget you spend, not a bucket you fill.

What that looks like in practice: • Retrieve guidelines per task, not per session • Size the payload to the model, not to the window • Measure tokens per completed task, not tokens per call • Cache the procedure that worked, not the documents behind it

An August 2026 arXiv paper reaches the same place from the other direction, reusing the past trajectory that fits the query instead of re-retrieving the raw context. https://arxiv.org/abs/2608.12847

Teams fix a context problem by adding more context, then read the bigger prompt as progress.

More context is easier to ship. Less context is what survives production.

Bigger windows solve capacity. Better selection solves reliability.

#ai #llm #context-engineering