context-engineering

To optimize AI agent context, you need dynamic context provisioning at session start, task switches, and quality drop recovery, ensuring agents receive necessary domain knowledge and correct patterns for high-quality output.

Dynamic context provisioning is a strategy that delivers necessary context to AI agents at session start, during task switches, and when output quality drops, organizing information into system prompts, core rules, and active skills.

You manage context hierarchy by organizing information into system prompts, core rules, background skills, and active skills, applying specific context loading strategies when tasks switch or output quality drops.

Yes, comprehensive context management supports integrating MCP tools and project files alongside rules and skills, providing broad context coverage to address missing domain knowledge and incorrect agent patterns.

You need to trigger context loading for quality drop recovery when an AI agent's output degrades, applying structured context management strategies to re-inject necessary domain knowledge and correct patterns.