--- sourceDocument: Yokohama Enable AI sourceDocumentLink: https://servicenow-prod.fluidtopics.net/r/yokohama/intelligent-experiences Release : - yokohama ft:locale : - en-US ft:publication_title : - Yokohama Enable AI ft:clusterId : - platai bundleId : - platai workflow : - Platform --- # Agentic evaluation run results # Agentic evaluation run results {#ariaid-title1} Release version: Yokohama Updated July 31, 2025 ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read Summarize ![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete. ## Summary of Agentic Evaluation Run Results Agentic evaluations assess the performance of AI agents and workflows by analyzing execution logs. The evaluation results page provides multiple metrics and scores that reflect task completeness and tool usage, enabling customers to gauge their agentic workflows' effectiveness. If an overall task completion evaluation is conducted, the results include recommended actions for improving or deploying the agentic workflows. Show full answer Show less ## Key Features * **Overall Score:** Each evaluation method yields an overall score with a percentage of successful evaluations, categorized as Excellent, Good, Moderate, or Poor. * **Customization:** Users can adjust metric thresholds for each performance label by selecting "Customize metric thresholds." * **Individual Record Evaluations:** Each task is scored individually based on metrics assessing task completion, tool performance, and tool calling accuracy. ## Key Outcomes * **Excellent (90%-100%):** The workflow is performing well; proceed with confidence. * **Good (70%-89%):** Some performance inconsistencies exist; deploy with caution. * **Moderate (50%-69%):** Investigate root causes for poor performance; significant tasks remain incomplete. * **Poor (0%-49%):** Major issues detected; do not deploy. By leveraging these insights, ServiceNow customers can enhance their AI agents' performance and ensure optimal workflow execution. Learn about agentic evaluation runs and the meaning behind different evaluation scores from the agentic evaluation results page. ## Agentic evaluations overview Agentic evaluations measure how well AI agents and agentic workflows are accomplishing their objectives. A Now LLM Service model judges the AI agent or agentic workflow based on the execution logs. The results page of an evaluation run shows multiple metrics and scores measuring task completeness and tool use. If you run an overall task completion evaluation, the results page shows recommended actions for the AI agent or agentic workflow. Recommended actions give you suggestions for deployment or improvement to help ensure that the agentic workflows that you deploy are performing up to your standards. For more information on AI agent usage and other analytics, you can review the [AI Agent Analytics dashboard](https://servicenow-prod.fluidtopics.net/V36tvKzz2w1IW3mOyqq2_g "Track the AI agent use and efficiency gain on your instance through the AI Agent Analytics dashboard. The dashboard can reveal trends in how AI agents are used to improve the time to resolution and the number of tasks closed.") in the AI Agent Studio. ## Evaluation results overview For each evaluation method that you execute, the results page displays an overall score for the agentic workflow with a percentage of successful record evaluations and a label of Excellent, Good, Moderate, or Poor. You can change the metric thresholds for each label by selecting Customize metric thresholds. {#aia-eval-metrics__entry__4} | Label | Description | Recommended action | Default threshold | |-|-|-|-| | Excellent | Tasks were consistently performed at a high standard. The agentic workflow is working well. | Proceed with confidence | 90%--100% | | Good | Most tasks were performed successfully, but some performance inconsistencies suggest areas for improvement. | Deploy with caution | 70%--89% | | Moderate | A significant number of tasks weren't fully completed. Performance is below the desired level. | Investigate the root causes of poor task completion | 50%--69% | | Poor | The agentic workflow is consistently failing to complete tasks adequately. Major issues are present. | Do not deploy | 0%--49% | [Table 1. Overall task completeness evaluation run results] ## Individual record metric scores Evaluations are run against the log tables of agentic workflow executions. Each record is individually scored for each evaluation plan that you run. Individual record evaluations are scored according to the following metrics. {#aia-eval-metrics__entry__23} | Number | Score | Description | |-|-|-| | 3 | Successful | The main task was fully completed. All subtasks were resolved, and the steps followed a logical sequence with no critical errors. | | 2 | Partially successful | The task was partially completed. Some subtasks remain unresolved or inefficiencies affected the process. | | 1 | Unsuccessful | The task wasn't completed. Critical subtasks were abandoned or unresolved or the execution failed entirely. | [Table 2. Overall task completeness record metric scoresThe overall task completeness metric assesses whether an AI agent successfully completes its assigned task. It evaluates the execution logs of the agent, ensuring that all required steps were taken and the task was logically and effectively completed.] {#aia-eval-metrics__entry__35} | Number | Score | Description | |-|-|-| | 1 | True | The right tool was chosen for the action in the plan. | | 0 | False | The right tool wasn't chosen. | [Table 3. Tool performance record metric scoresThe tool performance evaluation metric assesses an AI agent's ability to select the most appropriate tool for each step while completing a task.] {#aia-eval-metrics__entry__44} | Number | Score | Description | |-|-|-| | 1 | True | Input key completeness, input value completeness, and input format completeness were successful. | | 0 | False | One or more of input key completeness, input value completeness, or input format completeness wasn't successful. | [Table 4. Tool calling records metric scoresThe tool calling evaluation metric assesses whether an AI agent correctly constructs tool calls by validating the accuracy, completeness, and formatting of the inputs it provides.]