context-engineering

To manage agent context across sessions, you can use context engineering to curate what the AI sees. It applies hierarchical context loading during session starts and task switches to maximize output consistency.

The best way to load per-feature specs is through a hierarchical context loading strategy. This approach tailors the agent's reasoning for each workflow by prioritizing relevant source files and architecture specs to reduce noise.

You can persist project-wide rules by creating a rules file at the project root, similar to CLAUDE.md guidelines. Starting a session will then load these configured rules to drive agent behavior consistently across tasks.

Inconsistent outputs during task switches often stem from stale or conflicting signals in the conversation history. Context engineering provides guardrails for error handling and manages history accumulation to prevent this.

Yes, you can enforce error handling and validation rules using context engineering guardrails. This process surfaces test results and errors directly to the agent, enabling rapid iteration and quality improvement.

Yes, context engineering works without external dependencies. It relies entirely on creating local rules files and managing the hierarchical loading of your project's existing source files and specs.