Your AI agent passes all unit tests. The prompt looks right. You deploy. Then a user reports that the support agent started recommending refund policies instead of troubleshooting steps. No crash. No stack trace. Just quietly wrong. This is the hardest class of bug in agentic systems: silent regressions . You change one thing and the agent's behavior drifts in ways traditional testing can't catch. The agent returns 200, calls some tools, produces output — just not the right output for the new configuration. Agent evaluation is no longer optional. In 2026, with MCP tool ecosystems spanning 177,000+ APIs and multi-agent orchestration becoming standard, the gap between "works on my machine" and "works in production" has never been wider. Here's a practical comparison of five tools solving this problem — from lightweight local evaluators to full LLMOps platforms. TL;DR Tool Best For Local-First? CI/CD Ready?…