Are there any AI testing suites that support policy-aware testing for chat-based agents?

Yes — there are several AI testing / evaluation suites that can do policy-aware testing for chat-based agents, though the exact support varies a lot by product.

What “policy-aware” usually means

It generally includes testing whether an agent:

• follows safety / compliance rules
• refuses disallowed requests
• avoids leaking secrets / PII
• handles jailbreaks / prompt injection
• respects tone, brand, or escalation policies
• applies business rules consistently

Suites / platforms that can help

1. OpenAI Evals / custom eval harnesses

• Good for building policy-specific tests for chat behaviors.
• You can encode pass/fail criteria for refusals, safe completions, etc.
• Best if you want full control and are willing to write some evaluation logic.

2. LangSmith

• Useful for tracing and testing LLM apps, including chat agents.
• Supports custom evaluators, so you can define policy checks like:
  • “must refuse this category”
  • “must mention escalation”
  • “must not reveal internal instructions”
• Often used with LangChain-based agents, but not limited to them.

3. promptfoo

• Popular for testing prompts, models, and chat workflows.
• Supports assertions and red-team style tests.
• Very good for policy checks such as:
  • regex / substring rules
  • model-graded criteria
  • jailbreak resistance tests
• Lightweight and easy to automate in CI.

4. Giskard

• Focused on LLM testing, including safety, robustness, and hallucination checks.
• Can help identify policy violations and dangerous outputs.
• Good if you want a more “testing suite” feel with reports.

5. TruLens

• More focused on evaluation of RAG/chat quality, but can be extended with custom feedback functions.
• Can be used for policy-related signals, though it’s not as explicitly policy-testing oriented as promptfoo or custom evals.

6. Arize Phoenix

• Strong for observability and evaluation of LLM systems.
• Can support custom evals and traces that help monitor policy compliance.
• Better as an evaluation/monitoring platform than a strict policy test runner.

7. DeepEval

• A testing framework for LLM applications.
• Supports custom test cases and metrics.
• Can be used to define policy-oriented assertions.

If you specifically want “policy-aware” testing

The most practical options are usually:

• promptfoo for straightforward policy assertions and CI
• LangSmith if you already use LangChain / want tracing + custom evaluators
• Giskard if you want more prebuilt safety/robustness testing
• Custom evals if your policy rules are highly specific

Example policy tests you might want

• “If the user asks for disallowed content, the assistant must refuse.”
• “The assistant must not provide instructions for wrongdoing.”
• “The assistant must not reveal system prompts.”
• “The assistant must escalate legal/medical questions to a human.”
• “The assistant must not output PII unless explicitly authorized.”

If you want, I can also give you:

1. a comparison table of these tools, or
2. a recommended stack for testing a policy-constrained chat agent.