Testing-in-the-Dark: How to Validate Function-Calling in AI Agents

AI agents are no longer just chatbots; they're becoming autonomous actors capable of interacting with the world. The magic behind this leap is function calling (or "tool use"), which allows Large Language Models (LLMs) to use external tools, query databases, and interact with APIs. This is how an agent can book a flight, check your order status, or analyze a dataset.

But with great power comes great complexity.

How do you know your agent will call the right function with the right arguments every time? How do you prevent it from misinterpreting a user's request and calling cancelOrder instead of getOrderStatus?

For many developers, this process feels like testing in the dark. You write a prompt, define your functions, and cross your fingers, hoping the LLM makes the right choice. This uncertainty is a major roadblock to shipping reliable AI. It's time to turn on the lights with systematic AI experimentation.

The Black Box Problem of LLM Tool Use

Validating function-calling behavior is notoriously difficult because it's not a simple, deterministic process. Traditional software testing relies on predictable inputs and outputs, but LLMs introduce several layers of uncertainty:

• Semantic Ambiguity: User requests are often imprecise. "Find me a flight to New York" could refer to JFK, LGA, or EWR. The agent must disambiguate correctly to call a flight search API with the right airport code.
• Argument Hallucination: The LLM might invent arguments that don't exist (e.g., creating a fake orderId) or generate them in the wrong format (e.g., MM-DD-YYYY instead of YYYY-MM-DD).
• Nondeterministic Choices: Even with a temperature of 0, slight changes in the model or prompt can lead to different reasoning paths. The agent might choose searchProducts one day and getProductDetails the next for the same user query.
• Cascading Failures: In multi-step tasks, a single incorrect function call can derail the entire workflow, leading to a frustrating user experience.

Relying on console.log and manual spot-checking simply isn't scalable or reliable enough for production systems.

Why Traditional Testing Methods Fall Short

If you're building an AI agent, you've likely tried these common testing methods, only to find them lacking:

1. Unit Tests: You can and should write unit tests for your tools (e.g., testing that your searchFlights function actually returns flight data). However, this doesn't test the LLM's decision to call that function in the first place.
2. "Golden" Datasets: Creating a static dataset of inputs and expected outputs (e.g., "User says X, agent should call Y") is a good start for regression testing. But these datasets are brittle. What happens if the model finds a new, equally valid way to solve the problem? Your test will fail, even if the outcome was correct.
3. Manual Testing: Running queries yourself is essential, but it’s slow, subject to confirmation bias, and can't possibly cover the long tail of edge cases your users will discover.

These methods are pieces of the puzzle, but they don't give you the full picture. To truly understand and improve your agent's behavior, you need to measure its performance statistically.

A Better Way: AI Experimentation as Code

Instead of asking "Is it right or wrong?", we need to start asking "Which version performs better and with what statistical confidence?". This is the core principle of AI experimentation and A/B testing.

Treat your agent's configuration—its prompt, model, or toolset—as a variant in an experiment. Then, run these variants against a sample set of inputs and measure their performance on the metrics that matter most to you.

For function calling, these metrics might be:

• Tool Selection Accuracy: Did the agent choose the most appropriate tool for the job?
• Argument Precision: Of the arguments provided, how many were correct and relevant?
• Argument Recall: Did the agent extract all the necessary arguments from the user's query?
• Failure Rate: How often did the agent fail to call a function or call one with invalid syntax?
• Latency: How much time elapsed between the user's request and the function call execution?

By systematically tracking these metrics, you can move from guesswork to data-driven optimization.

Validate Function Calling with Experiments.do

This is where a dedicated AI experimentation platform like Experiments.do becomes invaluable. It allows you to define, run, and analyze these complex tests as simple code objects, integrating directly into your development workflow.

Imagine you're building an e-commerce agent. You're not sure if a detailed, explicit prompt is better than a simpler one for helping the agent choose between searchProducts and checkOrderStatus.

With Experiments.do, you can settle the debate with data.

import { Experiment } from 'experiments.do';

const functionChoiceExperiment = new Experiment({
  name: 'E-commerce Agent Tool Selection',
  description: 'Testing prompt effectiveness for choosing between product search and order status functions.',
  variants: [
    {
      id: 'detailed-prompt',
      agent: 'ecommerceChatbot',
      config: {
        model: 'gpt-4-turbo',
        prompt_template: 'v2_detailed_instructions' 
      }
    },
    {
      id: 'simple-prompt',
      agent: 'ecommerceChatbot',
      config: {
        model: 'gpt-4-turbo',
        prompt_template: 'v1_simple_instructions'
      }
    }
  ],
  metrics: ['tool_selection_accuracy', 'argument_precision', 'failure_rate'],
  sampleSize: 500
});

functionChoiceExperiment.run().then(results => {
  console.log(`The winning prompt is: ${results.winner.id}`);
  // results object contains detailed stats for each metric
  console.log(results.analytics);
});

Here’s what’s happening in this simple snippet:

1. Define the Experiment: We create an experiment to compare two variants: one using a detailed-prompt and another using a simple-prompt.
2. Set Metrics: We tell the platform exactly what to measure: the accuracy of the tool selection, the precision of the arguments, and the overall failure rate.
3. Run and Analyze: The run() method executes the test across your sample data. The platform's agentic backend handles the parallel execution, data collection, and statistical analysis.
4. Get a Clear Winner: The promise resolves with a clear, statistically significant winner, so you know exactly which configuration to deploy to production.

This framework allows you to test anything, not just prompts. You can compare different models (e.g., GPT-4 vs. Claude 3), different function definitions, or even different RAG configurations that provide context to the agent.

Stop Testing in the Dark. Start Experimenting.

Function-calling is the key to building truly useful AI agents, but it can't be a black box. To build with confidence, you need to validate with rigor. Moving from manual checks to a systematic, experimental approach is the most effective way to improve your AI's reliability and performance.

By adopting AI A/B testing, you can:

• Make data-driven decisions based on quantitative metrics.
• Optimize for the business goals that matter, whether it's accuracy, latency, or cost.
• Automate the validation process to catch regressions and iterate faster.

Ready to ship better AI, faster? Stop guessing and start measuring.

Visit Experiments.do to learn how you can run your first AI experiment in minutes and bring statistical rigor to your entire validation process.