The original title is: "The Evaluation Debt You Don't Know You Have: Why Agent Evals Fail in Production"

Short summary

The article introduces 'evaluation debt' — the gap between offline agent eval suites and real production behavior that causes unexpected failures post-deployment. Offline frameworks like LangSmith and Braintrust score against static snapshots that drift from live traffic, while multi-agent systems compound evaluation complexity exponentially. LLM-as-judge scoring introduces systematic biases including 50%+ error rates on complex tasks, making CI gating unreliable without complementary production monitoring.

• •Evaluation debt is the structural gap between offline eval suites and production behavior; 38% of AI teams call it their primary blocker
• •Multi-agent systems compound eval complexity — 10 agents create unpredictable emergent failure modes no individual eval catches
• •LLM-as-judge has known 50%+ error rates on complex tasks with position, length, and agreeableness biases undermining CI gating

Generated with AI, which can make mistakes.