How to Build Effective Evals for AI Agents
Introduction A common problem with AI agents is that their performance can seem worse after a change, without anyone knowing what caused it. The system prompt may have been changed, a tool description may have been updated, or the underlying model may have moved to a different version. Any of these changes can affect how the agent behaves.
- ▪Introduction A common problem with AI agents is that their performance can seem worse after a change, without anyone knowing what caused it.
- ▪The system prompt may have been changed, a tool description may have been updated, or the underlying model may have moved to a different version.
- ▪Any of these changes can affect how the agent behaves.
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| Original publisher | KDnuggets |
| Canonical URL | https://www.kdnuggets.com/how-to-build-effective-evals-for-ai-agents |
| Publication time | Wed, 16 Sep 2026 12:00:50 +0000 |
| Retrieval time | 2026-09-16T12:08:41.433Z |
| Last seen | 2026-09-16T12:08:41.433Z |
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| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | qI3xes3MvMUr · 1 stories |
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| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
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Opening excerpt (first ~120 words) tap to expand
Introduction A common problem with AI agents is that their performance can seem worse after a change, without anyone knowing what caused it. The system prompt may have been changed, a tool description may have been updated, or the underlying model may have moved to a different version. Any of these changes can affect how the agent behaves. Without a consistent way to measure those changes, it is easy to end up guessing and repeating tests manually. Evals provide a way to measure these changes. An eval gives an agent a task, runs it, and checks the result against a set of defined criteria. The same process can be repeated across different versions and changes, making it easier to spot differences.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at KDnuggets.
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