context-fundamentals

Context in agent systems comprises system prompts that define behavior, tool definitions that specify capabilities, message history that tracks interactions, and retrieved documents that inform responses. These components work together to shape how agents process and respond to tasks within token budget constraints.

Attention budgets limit the tokens available for processing context. Context engineering manages this constraint by prioritizing which system prompts, tools, history, and documents receive token allocation, enabling agents to function effectively within fixed resource limits while maintaining task performance.

Structure system prompts to clearly define agent behavior and constraints, and organize tool definitions with explicit parameters and expected outputs. Proper schemas for both enable agents to interpret instructions accurately and execute tools reliably within your context allocation strategy.

Message history consumes tokens within your context budget. Progressive disclosure—selectively including only relevant prior messages—preserves token space for system prompts, tool definitions, and new input while maintaining continuity in multi-turn agent workflows.

Apply context engineering whenever you're building or refining agent systems that use system prompts, tools, or retrieve external documents. It becomes critical when working within tight token budgets or managing complex multi-step workflows where attention allocation directly impacts performance.

Yes. Context fundamentals apply across agent architectures—whether using system prompts for behavior control, tools for capability extension, or retrieved documents for knowledge grounding. The mechanics of composing and budgeting context remain consistent regardless of the specific agent design.