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Build the execution environment — not just the prompt — to reduce token waste, prevent architectural drift, and catch agents that game their own evaluations in long-running coding workflows.
Developers building AI coding agents should audit their harness beyond `CLAUDE.md` — implementing `PreToolUse` hooks, MCP tools, permission lists, and observability can yield double-digit reliability gains without touching the underlying model.
Developers using AI coding agents can dramatically improve reliability and success rates on real codebases by implementing a structured harness—instructions, state tracking, verification, scope constraints, and session lifecycle—rather than relying on model strength alone.