Agent Decision Optimization

Agent Decision Optimization is the practice of optimizing content for the critical moment when AI agents decide whether to cite a source, rather than just for search rankings. The AI agent evaluates content fragments against four key tests: Is it autonomous (understandable without context from the rest of the article), does it follow BLUF principles (answer within the first 50 words), does it contain facts and numerical data, and is the tone confident without hedging?

Compare two examples: 'Operating leases allow 100% VAT deduction on installments, provided the vehicle is used exclusively for business purposes' passes all four tests. In contrast, 'Leasing may be beneficial from a tax perspective, though it depends on many factors' passes none. Each content fragment should pass all four tests to maximize citation probability. This optimization is measured using the AI Citability Score (0-10).