This is Part 6 of the Building Agentic AI series. So far, we’ve explored building paths, tools, memory, and connecting agents to the world. Now it’s time to ensure your agent actually works as intended. Testing, evaluation, and iterative improvement are critical before trusting an AI agent with important tasks.

Why Testing Matters

Even the most promising agent can fail without proper testing. Bugs, poor logic, or unexpected inputs can cause unreliable behavior. Careful testing helps you:

• Catch issues early.
• Measure accuracy and reliability.
• Identify areas for improvement.

Testing Methods

• Unit Tests: Check individual functions or components of your agent’s code.
• Scenario Tests: Run the agent through realistic tasks to see how it performs in real-world conditions.
• Edge Case Tests: Try unusual or extreme inputs to test resilience.

Evaluation Criteria

When evaluating performance, consider:

• Accuracy: Does the agent produce correct results?
• Consistency: Does it behave predictably across multiple runs?
• Efficiency: Does it complete tasks in a reasonable time?
• User Experience: Is it clear, responsive, and helpful?

Improvement Strategies

• Refine Prompts: Adjust instructions and context for better results.
• Improve Data Quality: Ensure your agent has accurate and up-to-date inputs.
• Expand or Limit Actions: Give the agent more tools if it’s too limited, or remove tools if it’s making errors.
• Incorporate Feedback: Gather user feedback and adapt the design accordingly.

Example: Iterative Improvement Loop

1. Define success criteria.
2. Run tests and collect data.
3. Identify weaknesses.
4. Make targeted adjustments.
5. Repeat testing until performance stabilizes.

Real-World Application

For my SDG ecosystem project, I could test a research agent by giving it a list of tasks like “find new organizations in Barrie working on sustainable food initiatives” and then manually verify its results. Over time, improvements could focus on better search queries, higher-quality sources, and more relevant filtering.

Coming Next

In the next post, I’ll look at deploying and maintaining your agent — making sure it’s available when you need it and continues to perform reliably over time.