Skip to main content

Documentation Index

Fetch the complete documentation index at: https://agent-contracts.relari.ai/llms.txt

Use this file to discover all available pages before exploring further.

​
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
Prompting Tradeoff Light 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: Role of Contracts Light

​
How Agent Contracts Work

Agent Contracts follow a three-part approach:
  1. 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
  2. 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
  3. 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.

​
Next Steps

Want to dive deeper? Check out the Agent Contracts Whitepaper Try for yourself? Start with our Installation or explore Core Concepts to learn more.