AI agents offer powerful automation through their ability to reason, plan, and use tools. However, they create a fundamental challenge:We lack a structured way to specify agent behavior and ensure reliability.Unlike traditional software with explicit code, AI agents powered by LLMs operate on natural language prompts, creating critical gaps:
Imprecise specification: Prompts are ambiguous and can’t cover all scenarios
Limited reliability: There’s no built-in way to verify if LLM-powered agents follow instructions
Flexibility-control tradeoff: Detailed prompts reduce adaptability; general prompts reduce predictability
This forces developers to choose between agents that are too rigid or too unpredictable. Neither option works well for production, business-critical systems, which require both reliability and flexibility.
Agent Contracts provide a structured framework that addresses these challenges by complementing (not replacing) prompts with scenario-based specifications:
Define – Specify expected behavior through contracts
Write natural language contracts for specific scenarios
Express business logic as verifiable conditions
Complement general prompts with precise requirements
Verify Offline – Test agent traces against contracts offline
Analyze execution traces to verify contract compliance
Measure performance across test scenarios
Debug and improve contracts before deployment
Certify in Runtime – Apply contracts during runtime execution
Check agent behavior against contracts in real-time
Enforce contract conditions when violated
Generate execution traces for continuous improvement
This structured approach bridges the gap between natural language instructions and reliable execution, addressing both specification and reliability challenges simultaneously.