Introducing Human-in-the-loop Evaluation for Agentic AI Observability

AI observability tools monitor metrics like uptime, latency and costs. But they don’t tell you enough about the quality of agentic outputs or how to take action to improve results. That gap is the last-mile problem for agentic AI evaluation.

Today we’re solving the last-mile problem with ready-to-use interfaces that bring human insights into agent trace evaluation. Now you can integrate observability data from your AI observability tools into Label Studio Enterprise, including Braintrust, LangSmith and Langfuse. The result is a complete evaluation pipeline where context and actionable insights drive toward verifiable improvements.

UI features for human evaluation alongside agent traces

Here’s what you get out of the box in Label Studio Enterprise:

1. A fully designed interface for human expert evaluation on top of agentic AI traces. Powered by ReactCode, this interface is designed to give you useful structured data. The template includes:
   1. UI to filter and identify turns by User, Assistant (the agent) or Tool (for tool calling)
   2. Labels for pass / fail verdicts
   3. Issue tags
   4. Severity
   5. An open comment field to describe expected behavior
2. Ready-to-run scripts that convert traces from popular observability platforms into a format that’s compatible with Label Studio using the SDK

When you follow the step-by-step guide to run the scripts and publish the template, you get a state-of-the-art evaluation interface that enables domain experts to add context agent traces as structured annotation tasks.

Get started now:

→ Braintrust

→ Langfuse

→ LangSmith

Take observability further in Label Studio Enterprise

Beyond the templates for agentic AI evaluation, you can use other aspects of the Label Studio Enterprise platform to speed up time-to-insight.

For example, you can use Prompts in Label Studio Enterprise to pre-label tasks. With this workflow, you can use AI-generated data to quickly discern which turns in your agentic user flows are likely to be the most problematic. This way, you can maximize the time and attention of domain experts, to evaluate only the most complex or nuanced parts of a back-and-forth interaction.

Label Studio Enterprise also includes analytics. With label distribution reports, you can analyze which labels and verdicts have been applied across sets of traces. These reports and visualizations let you spot issues and monitor trends at scale. You can understand whether a deployed change improves quality or reduces the number of instances of a specific issue.

Get started now

If you’re already up-and-running on Label Studio Enterprise, you can visit the tutorials library and select any of the observability templates to get started today.

If you’re coming from Label Studio’s open-source edition or new to Label Studio Enterprise, we’d love to connect with you directly so we can learn more about your evaluation initiatives. Experts on the HumanSignal team are standing by. Book a time to talk with us today.