The Fundamental Challenge with AI Agents
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.
The Solution: Agent Contracts
Agent Contracts provide a structured framework that addresses these challenges by complementing (not replacing) prompts with scenario-based specifications:
How Agent Contracts Work
Agent Contracts follow a three-part approach:-
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