context-engineering

Context engineering shapes prompts, retrieval payloads, memory summaries, and tool contracts into a single, well-structured context packet. This ensures predictable model behavior and auditable reasoning across multi-tool agent workflows.

You can structure retrieval payloads and memory by defining ordering, provenance, and budget rules within a context packet. This approach keeps system prompts deterministic, testable, and auditable during execution.

Design a context packet by combining a system role, task state, retrieved evidence, and tool contracts into a portable format. You can use the render_context_packet.py script as a starter reference for deterministic execution.

Yes, context engineering applies directly to RAG workflows and memory management. It enforces budget rules and tool contracts to improve model reliability and safety when handling retrieved evidence and memory summaries.

The best way to keep system prompts deterministic is to enforce a frontmatter with name and description, while applying strict ordering and provenance rules to your context packet for predictable agent behavior.

You should avoid a single context packet if your agent workflow lacks defined tool contracts or retrieval payloads. Without structured memory summaries and provenance rules, combining context may reduce model reliability.