chaos-labs-eval-creation

You can create evaluation test cases for AI wallet agents by defining a config structure with user personas, agent flows, and explicit expectations. This approach supports persona-based evals, objective testing, and rubric-driven criteria for consistent, repeatable testing.

Rubric-driven LLM evaluation is an assessment method using explicit criteria to judge agent behavior. It works by defining expected outcomes in an llm_eval block within an eval config, allowing sequences, timeouts, and rubric-based criteria to validate wallet-agent interactions.

Yes, you can use YAML to structure persona-based evals for wallet agents. The eval config uses YAML frontmatter for discovery and defines user, agent, and expect blocks, accommodating optional scripts, references, and assets for extended tooling.

Set up turn-based evaluation coaching by configuring sequences and timeouts in your eval config. This structure supports rubric-based llm_eval criteria to systematically assess multi-turn wallet-agent interactions against defined expectations.

Yes, Chaos Labs eval creation supports adding optional scripts, references, and assets to test cases. The eval config structure accommodates these extended tooling options alongside core user, agent, and expect blocks.

The best way to generate eval scenarios for security and compliance checks is using an automated eval creation tool that builds structured configs with personas, objectives, and rubric-driven expectations, enabling QA teams to rapidly produce comprehensive test suites.