Evaluating AI agents: moving past the vibe check

TL;DR

Your agent can loop, burn tokens, or give a completely hallucinated answer and still look fine. A vibe check won't catch that. This post is the playbook behind our AI Agent Clinic episode How to Actually Evaluate (Any) AI Agent: Beyond the Vibe Check, where we build an eval pipeline for a LangGraph agent in 60 minutes. Here's what's inside:

Evaluating AI agents: moving past the vibe check

When teams start building an AI agent, testing usually looks the same. You wire up a model, hand it a few tools, run three or four prompts in a terminal, and eyeball the output. If the answer looks reasonable, you ship it.

In practice, this breaks down fast. You tweak a system prompt to fix an edge case in one workflow, and suddenly the agent stops calling a required tool in another.

Worse, agents fail differently than standard software. An agent can get stuck in a retry loop, fire off multiple tool calls, yet still return a polite, plausible-sounding final answer.

In production, that hidden failure is costly:

• It quietly spikes your cloud bill and token usage.
• It exhausts downstream API rate limits and quotas.
• It leaves users staring at a loading spinner.
• It can trigger duplicate side effects in external systems, all while looking "fine" on the surface.
• It erodes user trust, and first impressions are hard to win back.