Context Engineering | Compaction & Agent Memory for Automated Malware Analysis

SentinelLABS has evaluated OpenAI's native context compaction feature for automated malware analysis, finding it significantly reduces token usage and costs without impacting overall task quality. Compaction compresses past context into a denser working state, which is crucial for long-running agent tasks where context can accumulate rapidly and degrade performance. While effective, the analysis noted a slight decrease in the model's ability to recover higher-level structural reasoning, underscoring the need to store critical artifacts in durable storage rather than relying solely on compacted context.