AI Agent Evaluations Table
This article provides a clear overview of evaluation (Evals) outcomes, expected versus actual behavior, and troubleshooting resources for AI agents in Tulip.
Overview
Systematic evaluation of AI agents is essential to ensure they are reliable, accurate, and meet enterprise-grade performance benchmarks, especially for deployment in production, regulated, or otherwise sensitive environments.
Evals tables are tools for measuring AI agent performance across a range of real-world tasks. By comparing expected versus actual outcomes in standardized scenarios, you can spot gaps, guide improvement, and track regression over time. Consistent use of Evals brings transparency, repeatability, and confidence to your agent lifecycle.
Why Use Evals?
- Reliability: Ensure agents behave as intended before widespread deployment.
- Traceability: Document decision-making and changes over time.
- Benchmarking: Quickly compare agents, prompts, or configurations.
- Troubleshooting: Track failures and monitor improvements release-over-release.
- Continuous Improvement: Provide a feedback loop for prompt and agent engineering.
Agent Lifecycle
- Create the Agent
Define the agent’s scope, tools, and initial prompt. - Run Evals
Test the agent against the prompts in the Evals table. - Document Performance
Record expected vs. actual results and pass/fail outcomes. - Refine the Prompt
Update or modify the prompt to cover additional use cases or improve behavior. - Re-run Evals
Test again using the same prompts and dataset to ensure fair comparison. - Iterate
Repeat this cycle until the agent consistently meets performance criteria.
How to Use Evals
- Define Clear Test Prompts:
Pick realistic user prompts or scenarios that the agent needs to handle. - Set Expected Results:
Write down precise, measurable success criteria for each scenario. - Run the Agent:
Test the agent using each prompt under controlled conditions. - Document Actual Results:
Record the actual output or behavior observed from the agent. - Pass/Fail Assessment:
Did the agent meet the criteria? Mark accordingly. - Add Comments:
Note down gaps, unexpected behaviors, or improvement ideas. - Create a Golden Dataset: A curated set of examples covering the agent’s core use cases. Test every prompt change against this dataset and promote it to production only if the metrics improve; otherwise, iterate further.
How to Read the Evals Table
| Column | Description |
|---|---|
| Prompt | The user input or scenario given to the agent (what you test). |
| Expected Result | The specific, correct output you expect from the agent in this situation. |
| Actual Result | What the agent actually output or did in response to the prompt. |
| Pass/Fail | Pass if expected and actual results match; Fail otherwise. |
| Notes/Comments | Any explanation, troubleshooting info, or context for the result. |
Example Evals Table
| Agent Name: Example QA Assistant |
|---|
| Prompt | Expected Result | Actual Result | Pass/Fail | Notes/Comments | |
|---|---|---|---|---|---|
| "Summarize yesterday’s log." | Concise, accurate summary with key metrics. | Summary accurate, missed downtime section. | Fail | Improve prompt for downtime info. | |
| "Find all open POs for Line 3" | Correct list of open POs for Line 3. | Returned all open POs and was accurate. | Pass | ||
| "Generate end-of-day report." | Complete report including QA events. | Missed QA events section. | Fail | Review QA event data integration. |
Further Reading
Troubleshooting
If you encounter issues with any AI agents, have questions about evaluation criteria, or want to share feedback, visit the Tulip Community.