Agentic Context Engineering
The Evolution of Cognitive Architectures: A Comprehensive Analysis of Agentic Context Engineering 1. Introduction: The Contextual Bottleneck in Artificial Intelligence The advancement of Large Language Models (LLMs) has historically been defined by a relentless pursuit of scale. The governing hypothesis—scaling laws—dictated that increasing parameter counts and training data volumes would linearly (or exponentially) yield gains in reasoning capability and generalization. However, as the industry transitions from the era of static chatbots to the epoch of autonomous agents, a critical bottleneck has emerged that parameter scaling alone cannot resolve: the management of context . For an autonomous agent to function effectively in dynamic, long-horizon environments—such as software engineering, financial auditing, or legal discovery—it requires more than just raw intelligence; it requires a persistent, evolving understanding of its environment, its past actions, and the specific con...