Context Engineering Fundamentals

Context is the complete state available to a language model at inference time — system instructions, tool definitions, retrieved documents, message history, and tool outputs. Context engineering is the discipline of curating the smallest high-signal token set that maximizes the likelihood of desired outcomes. Every paragraph below earns its tokens by teaching a non-obvious technique or providing an actionable threshold.

When to Activate

Activate this skill when:

• Designing new agent systems or modifying existing architectures
• Debugging unexpected agent behavior that may relate to context
• Optimizing context usage to reduce token costs or improve performance
• Onboarding new team members to context engineering concepts
• Reviewing context-related design decisions

Core Concepts

Treat context as a finite attention budget, not a storage bin. Every token added competes for the model's attention and depletes a budget that cannot be refilled mid-inference. The engineering problem is maximizing utility per token against three constraints: the hard token limit, the softer effective-capacity ceiling (typically 60-70% of the advertised window), and the U-shaped attention curve that penalizes information placed in the middle of context.

Apply four principles when assembling context: