Catch AI Agent Failures Before They Ship | Harness AI Evals
AI agent quality should not depend on manual checks.
But for many teams shipping AI in production, agent failures are silent. The agent doesn't crash - it just gives confidently wrong answers, and your monitoring sees nothing wrong. Without automated guardrails, plausible-sounding wrong responses, hallucinations, and quality regressions reach customers before anyone notices.
In this walkthrough, Shibam Dhar, Developer Relations Engineer at Harness, shows how Harness AI Evals uses targets, golden datasets, metric sets, LLM-as-Judge scoring, and a native pipeline step to enforce AI quality across the software delivery lifecycle.
You'll see how Harness AI Evals:
- Defines reusable building blocks - targets, datasets, metric sets - that compose into an evaluation
- Scores agent responses with 50+ built-in metrics
- Runs evaluations as a native step in your Harness CI/CD pipeline
- Surfaces per-item, per-metric scoring with written reasoning explaining exactly why something passed or failed
- Diagnoses failures with AI Analysis, including severity-tagged recommendations and suggested fixes
- Tracks pass rate trends, score history, and item-level results across every run
If your team is shipping AI agents and wants to make sure they behave the way you expect every single time, this video is for you.
Learn more about Harness AI Evals:
📘 Request for the beta! – https://www.harness.io/demo/ai-evals
Chapters:
00:00 The problem with AI agents
00:42 What Harness AI Evals does
01:02 Setting up an evaluation
03:30 Running the eval
04:00 Results: what passed, what failed, and why
06:02 Get started
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