Meta Context Budgeting

Techniques for managing the context window limit in long-running AI agents. Use when this capability is needed.

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Meta-Context Budgeting

AI models have a "budget" (the context window). Every token you use leaves less room for reasoning.

Budgeting Strategies

  • Purging: Identifying and removing outdated logs, repetitive instructions, or completed task summaries once they are no longer needed.
  • Summarization: Compressing long technical discussions into concise action points before proceeding to the next phase.
  • Priority Filtering: Keeping "System Instructions" and "Foundational Constraints" at the highest priority, while ephemeral chat history is lower priority.

Tools

  • Token Counters: Using tools to monitor current usage.
  • Selective Retrieval: Only fetching the most relevant "chunks" of a codebase or skill library.

Best Practices

  • Clean State: Occasionally starting a fresh session and only carrying over the "Essential State" (ADRs, current plan, critical constraints).

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tomevault-io/skills-registry/tree/main/jcorpac--ai-skills-library--meta-context-budgeting commit a7deecebf6

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