agent-harness-optimizer

Overview

Agent harness optimization is the practice of tuning the runtime environment that surrounds an AI agent -- model selection, prompt structure, hook configuration, memory persistence, and session management -- to maximize output quality while minimizing token cost and latency. Derived from real-world patterns across 10+ months of daily agentic work, these techniques apply to any harness: Claude Code, Cursor, OpenCode, Codex, Gemini, and beyond.

When to Use

• Token costs are rising faster than output quality
• Agents lose context between sessions or after compaction
• Hook scripts are slow, brittle, or produce noisy output
• You need the same agent behavior across multiple AI coding harnesses
• Session history grows unwieldy and needs structured management
• Background processes are eating into the main context window
• You want to set up continuous learning from session patterns

When NOT to Use