Business owners who are not finance experts can use AI as a powerful analysis tool. By feeding all invoices into an AI with a simple prompt, they can quickly identify spending trends, abnormalities, and financial patterns without needing complex software or a dedicated finance team.

A practical, immediate use case for AI agents is automating routine tasks with financial implications. An agent tasked with ordering a daily lunch, for example, can automatically detect and flag a small price increase that a human would likely overlook, providing a subtle but consistent ROI.

Brex's automated expense auditing employs a multi-agent system. An "audit agent" is optimized for recall, flagging every potential policy violation. A second "review agent" then applies judgment and business context to decide which cases are significant enough to pursue.

Strict rules can be penny-wise and pound-foolish (e.g., saving on a hotel but losing a deal). The ideal is a shared cultural understanding—a "moral code"—where employees act like owners. Technology can provide context and transparency to foster this culture at scale.

Rather than programming AI agents with a company's formal policies, a more powerful approach is to let them observe thousands of actual 'decision traces.' This allows the AI to discover the organization's emergent, de facto rules—how work *actually* gets done—creating a more accurate and effective world model for automation.

To reliably translate a natural language policy into formal logic, Amazon's system generates multiple translations using an LLM. It then employs a theorem prover to verify these translations are logically equivalent. Mismatches trigger a clarification loop with the user, ensuring the final specification is correct before checking an agent's work.

Run HR, finance, and legal using AI agents that operate based on codified rules. This creates an autonomous back office where human intervention is only required for exceptions, not routine patterns. The mantra is: "patterns deserve code, exceptions deserve people."

Relying solely on natural language prompts like 'always do this' is unreliable for enterprise AI. LLMs struggle with deterministic logic. Salesforce developed 'AgentForce Script,' a dedicated language to enforce rules and ensure consistent, repeatable performance for critical business workflows, blending it with LLM reasoning.

Counterintuitively, Uber's AI customer service systems produced better results when given general guidance like "treat your customers well" instead of a rigid, rules-based framework. This suggests that for complex, human-centric tasks, empowering models with common-sense objectives is more effective than micromanagement.