AI models like Claude Code can experience a decline in output quality as their context window fills. It is recommended to start a new session once the context usage exceeds 50% to avoid this degradation, which can manifest as the model 'forgetting' earlier instructions.

When an AI agent like Claude Code nears its context limit where automatic compaction might fail, a useful hack is instructing it to "write a markdown file of your process and your progress and what you have left to do." This creates a manual state transfer mechanism for starting a new session.

Structure AI context into three layers: a short global file for universal preferences, project-specific files for domain rules, and an indexed library of modular context files (e.g., business details) that the AI only loads when relevant, preventing context window bloat.

AI plugins (MCPs) constantly consume valuable context window space, even when not in use. Integrating tools via Command-Line Interfaces (CLIs) is more efficient. The AI can execute local CLI commands as needed, providing full tool functionality without the persistent context overhead.

Providing too much raw information can confuse an AI and degrade its output. Before prompting with a large volume of text, use the AI itself to perform 'context compression.' Have it summarize the data into key facts and insights, creating a smaller, more potent context for your actual task.

When an AI's context window is nearly full, don't rely on its automatic compaction feature. Instead, proactively instruct the AI to summarize the current project state into a "process notes" file, then clear the context and have it read the summary to avoid losing key details.

When an AI model gives nonsensical responses after a long conversation, its context window is likely full. Instead of trying to correct it, reset the context. For prototypes, fork the design to start a new session. For chats, ask the AI to summarize the conversation, then start a new chat with that summary.

When deploying a complex AI agent like OpenClaw, the first step should be creating a visual dashboard. The default chat interface is a black box; a dashboard provides critical visibility into the AI's memory, skills, and scheduled jobs, making it manageable.

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.