# DigitalOcean updates agent evaluations in the Gradient AI Platform

DevFeed: [DigitalOcean updates agent evaluations in the Gradient AI Platform](<https://devfeed.tech/articles/evaluate-your-ai-agents-faster-and-more-effectively-19951.md>)

Original publisher: [Read original article](<https://www.digitalocean.com/blog/updated-agent-evaluations>)

Author: Grace Morgan

Published: 2025-12-04T16:20:20Z

Content type: release

Language: en

Sources: [DigitalOcean](<https://devfeed.tech/sources/digitalocean.md>)

Topics: [AI Platform](<https://devfeed.tech/topics/ai-platform.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [debugging](<https://devfeed.tech/topics/debugging.md>), [observability](<https://devfeed.tech/topics/observability.md>)

Tags: [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [ai-platform](<https://devfeed.tech/tags/ai-platform.md>), [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [debugging](<https://devfeed.tech/tags/debugging.md>), [observability](<https://devfeed.tech/tags/observability.md>), [product-updates](<https://devfeed.tech/tags/product-updates.md>), [rag](<https://devfeed.tech/tags/rag.md>), [security](<https://devfeed.tech/tags/security.md>)

## AI overview

DigitalOcean has updated the agent evaluations experience in its Gradient AI Platform. The changes include goal-oriented metric groups, example datasets, clearer upload errors, and trace-linked results to help developers evaluate and debug AI agents.

## Source excerpt

Evaluating AI agents can be tricky, especially when your tools aren't built around how you think and work. That's why we're excited to announce that we've updated our agent evaluations experience in the DigitalOcean Gradient™ AI Platform. These improvements make it faster and easier to evaluate your AI agents, understand results, and debug issues. What's changed for agent evaluations? The original evaluations feature was powerful but presented friction points that made it hard for developers to adopt. This redesign tackles those challenges head-on: Goal-oriented metric grouping: Metrics are now organized into intuitive, goal-oriented groups such as Safety & Security, Correctness, and RAG Performance. The Safety & Security group is preselected to help developers get started quickly and confidently. Example datasets: A list of example data sets are now available for common evaluations. This allows developers to create their own datasets quickly and efficiently. Clear, persistent error messaging: Upload errors are now clear, persistent, and specific, with messages like "Validation Error: 'query' column is missing". Developers can easily understand and fix issues, reducing friction in the testing process. Interpretable results with trace integration: Results are organized by the same metric groups used in setup, with tooltips to explain each metric and its scoring. Deep integration with observability tools allows developers to jump directly from a low score to the full trace for fast debugging and improvement. Why you should use evaluations Evaluations help you test and improve your AI agents systematically, making it easier to identify issues and optimize performance. For those just getting started, the preselected Safety & Security metrics and dataset examples let you quickly check for common issues like unsafe or biased outputs, giving greater confidence in your agent's behavior. For those scaling their agents, custom test cases, specialized metric groups like RAG Pe