Explore agentic evaluations
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Summary of Explore agentic evaluations
Agentic evaluations are automated tests designed to assess the readiness of your agentic AI assets, such as AI agents and agentic workflows, before deploying them to production. These evaluations use defined datasets and large language model (LLM) judges to objectively score key quality metrics like task completeness, response accuracy, and tool usage. This approach eliminates guesswork in quality assurance, providing clear, explainable evidence of performance and offering targeted optimization recommendations for iterative improvement.
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Agentic evaluations can be conducted in non-production environments during testing phases, allowing you to validate your AI assets’ ability to meet benchmarks and standards without live deployment risks. By testing against representative datasets, you gain confidence in your AI agents’ real-world performance.
Users
- Agent builders: Developers or configurators in AI Agent Studio who run rigorous evaluations to ensure AI agent quality at scale.
- Platform administrators: Governance roles who assess evaluation results as evidence to approve agents for production deployment.
- AI leads and architects: Use evaluation outcomes for audit trails and to monitor quality metrics across multiple agents.
Automated evaluations workflow
The process for agentic evaluations includes:
- Configuring an evaluation with a name, AI asset version, selected metrics, and dataset.
- Executing the evaluation while LLM judges score the agentic responses.
- Analyzing results including judge scores, identified issues, and trace logs.
- Applying system-generated optimization recommendations to improve the AI asset.
- Triggering re-evaluations to validate improvements and ensure ongoing quality.
Benefits
- Version-specific quality checks: Evaluate particular versions of AI assets to measure improvements or regressions.
- Customizable standards: Set custom metrics to align evaluations with your organizational benchmarks.
- Real-time progress tracking: Monitor ongoing evaluations to stay informed of status and interim results.
- Issue identification and traceability: Pinpoint problems in agentic responses and trace them back to underlying causes.
- Targeted optimization guidance: Receive actionable recommendations generated by the system to enhance agent performance.
Automated evaluations test your agentic AI assets and help determine when they're ready for production. Learn more about how evaluations work, who they’re designed for, and the benefits they deliver.
Agentic evaluations overview
Automated agentic evaluations help give AI agent builders the confidence to deploy with objective, explainable evidence that their agents are ready for production. They remove the guesswork from quality assurance by running your agent against a defined dataset and applying LLM-powered judges to score quality, such as task completeness, response accuracy, and tool use. From there, the system generates recommended optimizations you can apply before triggering a re-evaluation to confirm improvements.
Building agentic AI assets like AI agents and agentic workflows is an iterative process. Agentic evaluations are designed to verify the quality of the AI asset with in a structured way to help speed up the process. Because you're testing against representative datasets, you can be more confident in the performance of your agentic AI asset to handle real-world situations.
Agentic evaluations can be run in non-production environments and don't require live deployment. They can be run during testing phases of agentic AI assets to help ensure that they can be deployed to a production environment while meeting your benchmarks and standards.
Agentic evaluations users
| User | Description |
|---|---|
| Agent builder | Developer or configurator who builds agents in AI Agent Studio. Automated evaluations are designed so agent builders can run rigorous evaluations at scale. |
| Platform administrators | Platform administrators who govern which agents are approved for production can use automated evaluation results for evidence of quality before deployment. |
| AI leads and architects | AI leads and architects can use automated evaluation results for audit trails and quality metrics across multiple agents. |
Automated evaluations workflow
- Configure an evaluation run with a name, selected agentic AI asset and its version, metrics, and dataset.
- Execute the run and track progress as the LLM judges agentic responses.
- Analyze the run results, including the judge scores and identified issues and traces.
- Optimize the agentic AI asset with targeted recommendations, then trigger reevaluations.
- Validate the quality of future runs or other changes to the agentic AI asset.
Automated evaluations benefits
| Benefit | Feature | Users |
|---|---|---|
| Evaluate specific versions of agentic AI assets for quality | Execute an evaluation run | Agent builders |
| Set your own standards for agentic AI responses and performance | Custom metrics | Agent builders, Platform administrators, AI leads, and architects |
| Track evaluations as they progress | In-progress results | Agent builders |
| Identify issues and trace them back to the source | Evaluation outputs | Agent builders, AI leads, AI architects |
| Optimize agentic AI assets based on evaluation results | System-generated optimization recommendations | Agent builders |