context-engineer

Hierarchical agent systems orchestrate research, implementation, and validation sub-agents to manage complex tasks. This architecture supports structured note-taking and decision logging while preserving constraints across distributed reasoning workflows.

To design system prompts for just-in-time retrieval, configure your agent to fetch information on demand to fill knowledge gaps without bloating context. This requires calibrating the agent's role, responsibilities, and constraints to guide reasoning.

The best way to manage context compaction is to compress conversation history while preserving decisions and constraints. This is achieved through structured note-taking patterns and maintaining a dedicated decision and domain log.

Yes, you can use sub-agent architecture for complex tasks like code review, research planning, and decision logging. It supports hierarchical orchestration of research, implementation, and validation sub-agents to maximize reasoning quality.

Agent reasoning quality drops at token limits due to context bloat and lack of compaction. Applying just-in-time retrieval triggers, structured note-taking, and right-altitude system prompts maximizes reasoning capacity within the token budget.