context-engineering

Context engineering structures hierarchical information loading for AI agents, ensuring they see the right data at the right time. By preventing information overload or gaps, it maintains consistent context and reduces hallucinations during tasks.

To set up context for a new AI agent coding session, instruct the agent to load a persistent rules file and relevant project documentation before starting the task. This establishes curated baseline conventions and enforces project standards.

Yes, you can dynamically update agent rules and context when switching tasks. The system supports per-session and per-task adaptation, allowing you to adjust sources as sessions evolve without losing baseline project-wide conventions.

Hierarchical context loading improves agent output quality by structuring data from project-wide rules down to architecture docs and task-specific sources. This controlled layering ensures agents focus on relevant information, preventing drift in behavior.

Apply context engineering to your AI workflow during session starts, task switches, and project changes. This maintains consistent context and prevents information gaps that lead to hallucinations and unreliable agent behavior.