When building Spiral, a single large language model trying to both interview the user and write content failed due to "context rot." The solution was a multi-agent system where an "interviewer" agent hands off the full context to a separate "writer" agent, improving performance and reliability.

The significance of a massive context window isn't just about processing more data. It enables AI to identify and synthesize relationships across thousands of pages of disparate information, revealing insights and maintaining consistency in a way that's impossible with a piecemeal approach.

Even models with million-token context windows suffer from "context rot" when overloaded with information. Performance degrades as the model struggles to find the signal in the noise. Effective context engineering requires precision, packing the window with only the exact data needed.

Long-running AI agent conversations degrade in quality as the context window fills. The best engineers combat this with "intentional compaction": they direct the agent to summarize its progress into a clean markdown file, then start a fresh session using that summary as the new, clean input. This is like rebooting the agent's short-term memory.

Instead of just expanding context windows, the next architectural shift is toward models that learn to manage their own context. Inspired by Recursive Language Models (RLMs), these agents will actively retrieve, transform, and store information in a persistent state, enabling more effective long-horizon reasoning.

Unlike ChatGPT's Custom GPTs which often "forget" past interactions, Claude's "Projects" feature builds a persistent memory. It learns from all previous threads within a project, layering that knowledge on top of initial instructions to improve its output over time.

Overloading LLMs with excessive context degrades performance, a phenomenon known as 'context rot'. Claude Skills address this by loading context only when relevant to a specific task. This laser-focused approach improves accuracy and avoids the performance degradation seen in broader project-level contexts.

"Context Engineering" is the critical practice of managing information fed to an LLM, especially in multi-step agents. This includes techniques like context compaction, using sub-agents, and managing memory. Harrison Chase considers this discipline more crucial than prompt engineering for building sophisticated agents.

To make agents useful over long periods, Tasklet engineers an "illusion" of infinite memory. Instead of feeding a long chat history, they use advanced context engineering: LLM-based compaction, scoping context for sub-agents, and having the LLM manage its own state in a SQL database to recall relevant information efficiently.