Galileo is now part of Cisco

Sep 19, 2025

How to Test AI Agents Effectively

Conor Bronsdon

Head of Developer Awareness

Unlock the key to AI agent testing with our guide. Discover metrics, best practices, and innovative techniques to evaluate your AI agents.

AI agents are now transforming industries from customer service to coding assistance, but their power comes with risk. McDonald's AI agent drive-thru system once confidently added bacon to customers' ice cream orders and accumulated 260 chicken McNuggets when trying to cancel, forcing the fast-food giant to abandon the project after viral customer complaints.

Failures like this stem from the agent misinterpreting data and bypassing verification protocols, issues that proper agent testing would have caught. To prevent such costly errors, AI agents require specialized approaches to assess their decision-making, tool usage, and ability to handle ambiguous situations.

If you want reliable AI agents, this article outlines a seven-step structured approach to testing AI agents across their entire lifecycle. You'll learn how to define clear objectives, leverage appropriate benchmarks, and build agents that are trustworthy and reliable in real-world conditions.

Introducing Eval Engineering: Turn Evals Into Production Guardrails - YouTube

Agent testing step #1: Define objectives and process structure

Begin by applying Specific, Measurable, Attainable, Relevant, and Time-bound (SMART) criteria to your agent testing goals. Clear objectives connected to business outcomes provide the foundation for meaningful evals.

Whether you're developing a customer service agent, or a specialized tool, your testing approach must align with concrete, quantifiable targets.

What exactly should your agent accomplish? Define success metrics for each key function, establishing baseline performance expectations and improvement thresholds. For example, a customer service agent might target 95% accurate responses, a 30-second average resolution time, and a less than 2% escalation rate.

Break down your AI agent into testable components to enable targeted evals. Examine each element individually:

• Foundation model performance: How accurately does the base LLM understand inputs?
• Tool selection accuracy: Does the agent choose appropriate tools for specific tasks?
• Planning coherence: Can the agent create logical, sequenced steps to solve problems?
• Multi-turn conversation handling: Does the agent maintain context across interactions?
• Error recovery capabilities: How does the agent respond when initial approaches fail?

Develop comprehensive test suites addressing both routine scenarios and edge cases. This wide coverage simulates real-world conditions and reveals how your agent handles unexpected situations.

Agent testing step #2: Use benchmark datasets

Benchmark datasets provide standard metrics for comparing AI performance. By using established data, you can assess where your AI agent stands relative to others. These benchmarks come with sample code, predefined splits, and standardized eval scripts for reproducible testing.

According to research, the BetterBench approach shows how these resources prevent data contamination and result manipulation.

Familiar datasets help you identify weaknesses and measure progress. The broader community has tested and vetted them, reducing uncertainty and enabling accurate comparisons. Benchmarking AI agents against these datasets helps you evaluate performance in real-world tasks.

But no single dataset covers all scenarios your AI might encounter. Off-the-shelf benchmarks fall short when your AI operates in specialized conditions. That's when custom datasets become essential.

The WorkBench dataset focuses on workplace tasks like sending emails and scheduling meetings. Developers found significant performance gaps among models, ranging from a 3% success rate with Llama2-70B to nearly 43% with GPT-4.

These findings show that custom datasets can reveal critical shortcomings that generic approaches miss.

Agent testing step #3: Simulation and testing

Simulations create controlled environments where you can test AI agents across numerous scenarios without real-world consequences. Observable testing environments let you track every agent decision, action, and outcome with complete transparency.

How can you see what your agent is actually doing? Projects like 𝜏-Bench offer conversation-based test scenarios with detailed logging of internal states, revealing the agent's reasoning process.

Multi-agent testing better reflects real-world deployment conditions. When agents interact with each other, you'll discover emergent behaviors and integration issues that single-agent tests miss.

After the simulation, it becomes crucial to take your agent to real users. Small-scale deployments with actual people provide authentic feedback on usability and performance. These human interactions often reveal issues that even sophisticated simulations cannot predict.

Agent testing step #4: Select the right testing framework

Selecting an appropriate testing framework is crucial for consistency and efficiency. Your chosen framework must accommodate AI's unique characteristics while providing structured evaluation methods.

Many teams default to adapting traditional software testing frameworks, but this approach often misses agent-specific challenges like non-deterministic outputs and contextual reasoning.

Purpose-built frameworks like AgentBench, LangChain Testing, and AutoGen Evaluation offer specialized tools for agent assessment, with built-in support for conversation flows, tool usage verification, and decision tree analysis.

Agent testing step #5: Human and automated evals

An effective evaluation process combines expert judgment with automated scoring to ensure comprehensive results. Domain experts validate whether an AI agent's actions are appropriate in real-world contexts.

Involving specialists keeps outputs aligned with practical needs, especially in complex fields like finance or healthcare.

Automated evaluations powered by specialized models like Galileo's Luna-2 Small Language Models provide efficient, scalable assessment.

Agent testing step #6: Performance metrics evals

An agent's success relies on interconnected evaluation metrics, such as accuracy, efficiency, and scalability.

Accuracy measures how often the model makes correct decisions. In applications like medical diagnostics, accuracy can influence patient outcomes.

Agent testing step #7: Safety and security evaluation

Implementing effective AI security measures focuses on protecting AI agents against threats and ensuring responsible behavior in high-stakes decisions.

Monitoring AI safety metrics aids in this process, helping organizations improve their security practices.

These agent testing strategies provide the foundation for building trustworthy AI systems that users rely on instead of questioning.