What it is
Synthetic data is computer-generated information that statistically resembles real production data without containing any actual customer details. It's used to train models, run test scenarios, and stress-test agents before they touch live data. Generation methods range from simple rule-based mimicry to advanced generative models.
Why it matters
It lets mid-market companies test and refine agents without exposing sensitive customer records or violating privacy regulations. It also solves the cold-start problem when historical data is too thin to train on.
Key components
- data generator
- privacy validation
- test scenarios
- statistical fidelity check
How it connects
Synthetic data feeds Agentforce Testing Center scenarios and RAG pipelines without risking exposure of real Data Cloud records.
Good to know
Validate that synthetic data preserves realistic edge cases — overly clean synthetic sets can hide gaps that show up in production.
Related terms
Agentforce Testing Center
Salesforce's built-in tool for testing Agentforce agents at scale against generated and real test cases before they go live.
AI Red Teaming
Deliberately attacking your own AI agent with tricky prompts and jailbreaks to find weak spots before customers or attackers do.
Data Poisoning
Manipulating the data an AI model learns from — training sets, fine-tuning examples, or knowledge-base documents — so the resulting model is biased, broken, or contains hidden behaviors.
RAG (Retrieval-Augmented Generation)
A technique that makes AI smarter by fetching relevant information from your data before generating a response. The AI "looks it up" instead of guessing.
