# Context Engineering | Compaction & Agent Memory for Automated Malware Analysis

DevFeed: [Context Engineering | Compaction & Agent Memory for Automated Malware Analysis](<https://devfeed.tech/articles/context-engineering-compaction-agent-memory-for-automated-malware-analysis-8312.md>)

Original publisher: [Read original article](<https://www.sentinelone.com/labs/context-engineering-compaction-agent-memory-for-automated-malware-analysis/>)

Author: Gabriel Bernadett-Shapiro

Published: 2026-07-02T13:00:02Z

Content type: article

Language: en

Sources: [SentinelLabs - We are hunters, reversers, exploit developers, and tinkerers shedding light on the world of malware, exploits, APTs, and cybercrime across all platforms.](<https://devfeed.tech/sources/sentinellabs-we-are-hunters-reversers-exploit-developers-and-tinkerers-shedding-light-on-the-world-of-malware-exploits-apts-and-cybercrime-across-all-platforms.md>)

Topics: [ransomware](<https://devfeed.tech/topics/ransomware.md>), [Threat Research](<https://devfeed.tech/topics/threat-research.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>), [API](<https://devfeed.tech/topics/api.md>), [LangChain](<https://devfeed.tech/topics/langchain.md>), [Claude](<https://devfeed.tech/topics/claude.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agents](<https://devfeed.tech/tags/agents.md>), [analysis](<https://devfeed.tech/tags/analysis.md>), [anthropic](<https://devfeed.tech/tags/anthropic.md>), [api](<https://devfeed.tech/tags/api.md>), [code](<https://devfeed.tech/tags/code.md>), [coding](<https://devfeed.tech/tags/coding.md>), [data](<https://devfeed.tech/tags/data.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [langchain](<https://devfeed.tech/tags/langchain.md>), [llm](<https://devfeed.tech/tags/llm.md>), [malware](<https://devfeed.tech/tags/malware.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [openai](<https://devfeed.tech/tags/openai.md>), [security](<https://devfeed.tech/tags/security.md>), [systems](<https://devfeed.tech/tags/systems.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

## AI overview

SentinelLABS evaluates OpenAI's native compaction in the Responses API for automated malware analysis. The evaluation found an approximately 86% reduction in input tokens with no measurable change in aggregate task quality, suggesting that compaction can reduce cost and context noise in long-running security workflows.

## Source excerpt

Compaction cut input tokens 86% across long-running agent evals with no quality loss. Context discipline matters as much as model selection.