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:

   markdown

   # 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:

   markdown

   <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