While Large Language Model (LLM) agents are increasingly capable in specialized domains, the impact of Context Window Constraints on reasoning stability remains under-explored. In this paper we investigate how strictly controlling context size influences agent performance in solving multi-step cryptographic Capture The Flag (CTF) challenges. We adapted an agentic environment where models attempt to solve crypto challenges under four distinct context length constraints (8k–64k), managed dynamically as a tunable hyper parameter. Our results reveal a non-linear performance curve with a clear saturation threshold between 16k and 32k to-kens, beyond which additional context offers negligible benefit. We observe a distinct shift in failure modes: tight constraints lead to context starvation (hallucination), while unconstrained windows allow the accumulation of error traces, where the presence of prior failed attempts biases the agent toward repetitive action loops which in turn inflates its operating costs and decreases operational efficiency. These findings demonstrate that unconstrained context is not only expensive, but leads to regression in the reasoning stability within logic-heavy domains. We conclude that future bench-marks must explicitly distinguish between capability failures and context-induced failures. Further-more, our findings suggest that engineering strategies should prioritize dynamic context management over the reliance on static, maximized windows. ... Read more