Context Fundamentals

Context Engineering Fundamentals

Foundational understanding of context engineering for AI agent systems, covering context components, attention mechanics, progressive disclosure, and context budgeting.

Prerequisites

• Understanding of LLM basics
• Familiarity with AI agent architectures
• Knowledge of token concepts

Instructions

1. Understand Context Components

   Context includes everything the model can attend to:

   • System Prompts: Core identity, constraints, behavioral guidelines
   • Tool Definitions: Actions an agent can take with descriptions
   • Retrieved Documents: Domain-specific knowledge loaded at runtime
   • Message History: Conversation and reasoning across turns
   • Tool Outputs: Results of agent actions (can be 80%+ of context)

2. Apply the Attention Budget Constraint

   • Models create n² relationships for n tokens
   • Attention "depletes" as context grows
   • Middle of context receives less attention than beginning/end
   • Place critical information at attention-favored positions

3. Use Progressive Disclosure

   Load information only as needed:

   # Instead of loading all documentation at once:
   
   # Step 1: Load summary
   docs/api_summary.md          # Lightweight overview
   
   # Step 2: Load specific section as needed
   docs/api/endpoints.md        # Only when API calls needed
   

4. Organize System Prompts

   Use clear section boundaries:

   <BACKGROUND_INFORMATION>
   You are a Python expert helping a development team.
   </BACKGROUND_INFORMATION>
   
   <INSTRUCTIONS>
   - Write clean, idiomatic code
   - Include type hints
   </INSTRUCTIONS>
   
   <TOOL_GUIDANCE>
   Use bash for shell operations, python for code tasks.
   </TOOL_GUIDANCE>
   

5. Practice Context Budgeting

   • Know effective context limit for your model
   • Monitor context usage during development
   • Implement compaction triggers at 70-80% utilization
   • Design for degradation rather than hoping to avoid it

6. Prefer Quality Over Quantity

   Find the smallest possible set of high-signal tokens that maximize desired outcomes. More context is not always better.

Error Handling

• If agent behavior is unexpected, check context composition
• If responses degrade mid-conversation, context may be overloaded
• Implement observation masking for long tool outputs

Notes

• Context engineering is iterative, not one-time prompt writing
• File-system access enables natural progressive disclosure
• Hybrid strategies work best: pre-load some, load more on demand
• Tool outputs often dominate context - design for this

Source: muratcankoylan/Agent-Skills-for-Context-Engineering