<![CDATA[The rapid evolution of autonomous AI agent workflows has introduced new challenges in software engineering, particularly regarding context token bloat and runtime security vulnerabilities within AI agent gateways. This study presents a benchmarking-based secondary analysis and computational modeling of the Progressive Disclosure method for optimizing context management in the OpenClaw AI Agent Gateway. Rather than conducting direct experimental implementation, the evaluation synthesizes empirical benchmark data from previous Model Context Protocol (MCP) studies, mathematical performance modeling, and runtime threat analysis reported in recent literature. The benchmarking results indicate that Progressive Disclosure has the potential to reduce upfront token overhead by approximately 95.6%, decrease session token consumption from 45,000 to 2,800 tokens, reduce estimated execution latency by 60.9% (from 12.49 s to 4.88 s), and lower projected operational costs by 93.8%, while maintaining high tool-selection accuracy. Furthermore, the threat modeling analysis identifies several critical runtime vulnerabilities within the OpenClaw environment, including the Claw Chain attack sequence involving CVE-2026-44112, CVE-2026-44113, CVE-2026-44115, and CVE-2026-44118, which collectively demonstrate the importance of strengthening runtime isolation beyond prompt-level optimization. The analysis indicates that Progressive Disclosure can significantly improve context efficiency and reduce computational overhead; however, robust low-level virtualization mechanisms remain essential for protecting autonomous AI agent infrastructures against advanced runtime attacks.]]>
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