Is Your Team Ready for Synthetic Data for AI Agents?

Do you have an idea for a chatbot but fear testing on live customers and GDPR? Synthetic data—'fake' examples of conversations created from your rules—can help. With it, you can check if your AI agent works before it interacts with real people and data.

Synthetic Data in a Nutshell: A Safe Simulator

Synthetic data consists of examples of conversations and tasks created by algorithms and machine learning models based on your guidelines and constraints. They do not contain real customer data. Think of it like a CPR mannequin: you practice procedures without risking a real patient.

An AI agent (a digital assistant that performs tasks autonomously) and a chatbot can learn from these scenarios based on synthetic data. A 'prompt' is simply a text command for the AI—like instructions for a new employee on what to do and how to communicate.

Recently, new materials on data generation (like Hugging Face's post on AutoSynthData) and tools for evaluating agents, such as DeepSeek Harness, have emerged. A 'harness' is a set of tests—a course that checks how well the agent performs in various situations. The takeaway: the industry wants to test quickly and safely.

A Simple Framework: From Scenario to Outcome

Here’s a plan that any team can understand—no coding required. First, you set the rules of the game, then practice on the 'simulator,' and finally measure two numbers that indicate whether it makes business sense.

• Step 1. Define the goal and boundaries. For example, 'the agent must complete a return in 3 steps, does not give discounts without approval, and speaks politely and concisely.' Boundaries are safety rules and style.
• Step 2. Create synthetic scenarios. Describe the customer's role, intent, required fields, and tone. Include challenging cases: missing order numbers, typos, and upset customers. It’s like a lesson plan for the bot.
• Step 3. Conduct tests on the 'simulator.' Use a set of scenarios like in a harness (a standardized set of trials). Check if the agent follows the steps, correctly asks for information, and stays within boundaries.
• Step 4. Measure two metrics. 1) Cost of successful tasks: how much you pay (tokens + human time) for a successfully completed case. 2) Question deflection: how many cases the bot closed without human involvement. Simple,
• hard data for decision-making.

GDPR in Practice: Test Without Sensitive Data

GDPR (European data protection regulations) does not prohibit AI but requires common sense. Synthetic data helps because you are not using real people. Still, it’s wise to follow a few safety rules.

Ensure 'data hygiene' from the start. This way, you avoid legal blocks when you're ready for a pilot with real customers.

• Do not include real names, ID numbers, or addresses in prompts. Use placeholders: Jan_123, Street_X.
• If possible, implement 'zero data retention' with your provider (meaning no saving of conversation content).
• Collect metrics, not content. Log only status: success/escalation, time, and cost.
• Limit access to tests. Grant permissions only to project members.
• Write a short risk note: purpose, scope, no personal data. This takes 20 minutes and organizes the topic.

Pitfalls and How to Avoid Them

Synthetic data is great for starting, but it has pitfalls. If the scenarios are too 'textbook,' the agent may perform well in tests but stumble in real life. Therefore, add 'messiness' and edges—just like in real life.

• Too nice of a world. Include an upset customer, slang, and typos.
• Lack of edges. Insert missing fields, contradictory information, and unusual invoice formats.
• Too few variations. Change the order of steps and tone of responses.
• No 'I don’t know' path. Establish when the agent should escalate to a human.
• No validation. Add simple checks: does the amount have the correct format, does the order number exist?
• Jumping to production without a pilot. Start with a small group of users and monitor the two metrics week by week.

Synthetic data allows you to train the agent like on a simulator: quickly, cheaply, and without GDPR risk. Start with a few real tasks, add challenging cases, and measure two metrics. Want to go through this step by step? Schedule a brief consultation—we'll help you build your first set of scenarios and a results dashboard.