# How Much Memory Does Your Agent Actually Need?

DevFeed: [How Much Memory Does Your Agent Actually Need?](<https://devfeed.tech/articles/how-much-memory-does-your-agent-actually-need-7262.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/ibm-research/altk-evolve-hmm>)

Author: Vatche Isahagian; Gaodan Fang; Jayaram Radhakrishnan; Punleuk Oum; Ashwath Vaithinathan Aravindan; Evelyn Duesterwald; G Thomas; Vinod Muthusamy; Merve Unuvar; Ayhan Sebin

Published: 2026-08-18T18:09:38Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [gpt-oss](<https://devfeed.tech/topics/gpt-oss.md>), [deepseek](<https://devfeed.tech/topics/deepseek.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [caching](<https://devfeed.tech/tags/caching.md>), [deepseek](<https://devfeed.tech/tags/deepseek.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [gpt-oss](<https://devfeed.tech/tags/gpt-oss.md>), [inference](<https://devfeed.tech/tags/inference.md>), [memory](<https://devfeed.tech/tags/memory.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [moe](<https://devfeed.tech/tags/moe.md>), [performance](<https://devfeed.tech/tags/performance.md>), [production](<https://devfeed.tech/tags/production.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

## AI overview

ALTK-Evolve equips agents with agentic memory by distilling reusable guidelines from past trajectories and injecting them at inference time without weight updates or human annotation. An evaluation across eight models found that the effective amount of memory varies by model: stronger models benefited from full guideline sets, weaker models from compact cores plus task-specific retrieval, and saturated models showed no measurable improvement. For gpt-oss-120b, selective retrieval improved task completion by 16.1 percentage points with only a 5% token increase.

## Source excerpt

Equipping an agent with agentic memory sounds simple: distill lessons from its past work, put them back in context, and more experience should mean better performance. It doesn't always work that way. When we scaled the evaluation to eight models -- from a 30B dense model to frontier proprietary systems -- one finding stood out: Agentic memory is not a feature you switch on. It's a dose you calibrate to the model.