ClientCoded Ships 40 Pre-Built Test Environments for AI Data Agents
Testing whether an AI agent returns correct answers against a real database is hard. You need data, you need to know what the right answer is, and you need queries that will trip the agent up. ClientCoded tries to solve all three at once with pre-built synthetic environments for 40 platforms, including Salesforce, Jira, Stripe, GitHub, Shopify, and Zendesk.
The core idea is simple. You describe your schema, either by connecting a staging database or just writing down the structure. ClientCoded generates a synthetic dataset that matches, creates 200 adversarial queries across seven categories, and computes the correct answer for every one. Because they generate the data, they know the ground truth. Your real data never leaves your systems, only the shape of it.
Seven Ways to Break an Agent
The adversarial queries are grouped by the kind of failure they are designed to expose. Clean queries test basic retrieval. Ambiguous queries force the agent to interpret vague language. Multi-step queries require joining information across tables. Scope boundary queries ask for information that does not exist in the data. Contradictory queries present conflicting conditions. Invalid assumption queries rely on false premises. Context-dependent queries require understanding the surrounding conversation.
Each category targets a different weakness. An agent that handles clean lookups perfectly may still fail when asked a contradictory question like "show me all resolved alerts that are still active." The correct response is to recognize the contradiction, not to return a filtered list that happens to match part of the query.
Business Rules as Ground Truth
Every environment ships with business rules that define what counts as correct. A monitoring platform environment states that resolved alerts must have a resolved_at timestamp, error logs should be less than 5 percent of total logs, and SLO error budgets must be between 0 and 100. A code hosting environment requires closed issues to have a closed_at value and merged pull requests to have merged set to true.
These rules are not just documentation. They are enforced when computing ground truth. If an agent claims a bill is paid but the paid_at field is empty, that is a correctness failure. The scoring system catches it.
What the Score Actually Shows
The scoring report includes an overall score, answer correctness broken down per question, conversation quality, and the exact questions the agent got wrong. One example from the documentation shows an agent that answered "14 open bugs" to a question about backlog bugs, when the correct answer was 23. The agent had filtered by status and missed tickets past their due date with no status update. The answer was fluent, specific, and wrong.
The full report includes per-question transcripts, a failure-type distribution showing which adversarial categories caused the most problems, and conversation-quality scoring. This is not a toy benchmark. It is a diagnostic tool that tells you exactly where your agent breaks and why.
40 Environments, Ready to Test
The pre-built environments cover a wide range of SaaS platforms. Monitoring tools, project management systems, accounts payable platforms, CRMs, knowledge bases, communication platforms, customer support systems, code hosting, DevOps, file storage, and recruiting. Each ships with a schema, a synthetic dataset of 1,500 to 2,500 rows, 200 adversarial queries, and computed ground truth.
For platforms not on the list, ClientCoded generates custom environments from your schema. The same pipeline applies: synthetic data, adversarial queries, ground truth, and a scored report.
The company is clear about what this is not. They are not affiliated with, endorsed by, or sponsored by any of the companies whose platforms they simulate. The environments contain synthetic test data only. The value is in the testing methodology, not in reproducing anyone's production data.