Context Engineering
Context Engineering is the practice of designing, versioning, and optimizing the information provided to an LLM or AI agent to ensure deterministic and high-quality outputs.
Core Pillars
- Static Context: System prompts, core logic, and behavioral guardrails.
- Dynamic Context: Real-time data, file states, and user intent.
- Retrieval Context: RAG-based data fetched from external knowledge bases.
The Context Development Lifecycle (CDLC)
Similar to DevOps, Context Engineering requires:
- Versioning: Tracking changes to prompts.
- Testing: Using “evals” to measure performance.
- Observability: Monitoring how context influences agent decisions.
SubAgents as Context Boundary Enforcement
The most powerful application of context engineering at scale is SubAgent Delegation: instead of engineering a single massive context window, you engineer which context each subagent receives. The orchestrator’s context contract becomes: “provide the minimal, maximally-relevant context per agent.” This transforms context engineering from a single-window optimization problem into an architecture of isolated, well-scoped cognitive units.
Custom subagents make this concrete: the tools field enforces which inputs the agent can pull in; the description field constrains which tasks trigger it; the system prompt body defines the cognitive frame. The memory: field adds a persistent knowledge layer that accumulates across sessions without inflating live context.
Related: LLM Observability, AI-Augmented SDLC, Claude SubAgents