Agentic AI Context Engineering: Patterns, Offloading Strategies, and Context Window Management for Autonomous Systems
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Abstract
Agentic AI systems represent a paradigm shift in artificial intelligence, where autonomous agents operate with minimal human oversight while maintaining contextual awareness across extended interactions. This paper provides a comprehensive survey of context engineering techniques for agentic AI systems, focusing on context window management, offloading strategies, and design patterns that enable scalable autonomous operation. We examine the theoretical foundations of context management, analyze current approaches to context preservation and compression, and present a taxonomy of context offloading patterns. Our review encompasses memory architectures, attention mechanisms, and hybrid approaches that balance computational efficiency with contextual fidelity. Through systematic analysis of existing methodologies, we identify key challenges including context degradation, semantic drift, and scalability limitations. This work serves as a foundational reference for researchers and practitioners working on autonomous AI systems that require sophisticated context management capabilities.
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Abstract
Agentic AI systems represent a paradigm shift in artificial intelligence, where autonomous agents operate with minimal human oversight while maintaining contextual awareness across extended interactions. This paper provides a comprehensive survey of context engineering techniques for agentic AI systems, focusing on context window management, offloading strategies, and design patterns that enable scalable autonomous operation. We examine the theoretical foundations of context management, analyze current approaches to context preservation and compression, and present a taxonomy of context offloading patterns. Our review encompasses memory architectures, attention mechanisms, and hybrid approaches that balance computational efficiency with contextual fidelity. Through systematic analysis of existing methodologies, we identify key challenges including context degradation, semantic drift, and scalability limitations. This work serves as a foundational reference for researchers and practitioners working on autonomous AI systems that require sophisticated context management capabilities.
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Surya Rao Rayarao, Naga Donikena.
Agentic AI Context Engineering: Patterns, Offloading Strategies, and Context Window Management for Autonomous Systems. Authorea. 09 September 2025.
DOI: https://doi.org/10.22541/au.175743558.85037187/v1
DOI: https://doi.org/10.22541/au.175743558.85037187/v1
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