Problem solved

Developers and organizations building AI agents often struggle to anticipate how their agents' prompts will perform under a wide variety of real-world user inputs, including adversarial or edge-case scenarios. Agent proof solves this by providing an automated stress-testing service that identifies vulnerabilities, weaknesses, and potential failure points in an agent's prompt before deployment, helping to ensure reliability, safety, and a positive user experience in production.

Use cases

• Testing a customer support chatbot against rude, confusing, or nonsensical messages to ensure it maintains professionalism and accuracy.
• Checking if a coding assistant can be tricked into generating insecure or malicious code when faced with cleverly crafted prompts.
• Validating that a healthcare Q&A agent does not provide dangerous or misleading advice when asked hypothetical or edge-case medical questions.
• Ensuring an AI sales agent stays on script and doesn't make unauthorized promises when users ask off-topic or leading questions.
• Evaluating an educational tutor agent's robustness to spelling errors, slang, and completely irrelevant queries to keep the learning experience on track.

Audience

• Developers
• ML Engineers
• Data Scientists
• Product Managers

Pros

• Automates what would otherwise be a manual and time-consuming testing process.
• Helps prevent costly prompt failures and brand damage in production.
• Simulates a wide variety of adversarial and edge-case inputs.
• Provides actionable insights and suggestions for prompt improvement.

Cons

• May not cover every possible real-world edge case, requiring additional manual testing.
• Paid-only model could be a barrier for individual developers or small projects.
• Effectiveness depends on the platform's ability to generate truly challenging test scenarios.