How to Set Rules That All AI Agents Must Follow

Types of Rules in a Multi-Agent System

Rules in a multi-agent system fall into several categories, each serving a different purpose:

Behavioral rules define how agents should act in specific situations. "Never share customer personal information outside the customer service agent." "Always verify statistics from at least two sources before citing them." "Never publish content that mentions competitor products by name without human review." These rules override any learned patterns or AI model tendencies.

Quality rules define the standards that agent output must meet. "All articles must be at least 1,000 words." "Code changes must pass all existing tests before being marked as complete." "Customer service replies must address the specific question asked, not provide generic responses." Quality rules ensure consistency as the system scales.

Escalation rules define when agents should stop and ask for human help. "Escalate any customer complaint that mentions legal action." "Flag any code change that touches the payment processing module." "Request human review for any content about medical or legal topics." These rules prevent agents from making decisions in areas where the risk of error is too high.

Scope rules define the boundaries of each agent's authority. "The content agent can publish blog posts but not change the homepage." "The coding agent can modify files in the application directory but not the infrastructure configuration." "The marketing agent can schedule emails but not change pricing." Scope rules prevent agents from accidentally affecting systems outside their domain.

How Rules Are Enforced

Rules are not suggestions. They are loaded into every agent's context at the start of every task execution, alongside the agent's system prompt and the current task details. This means rules are always present, regardless of what the agent is working on or what it has learned through experience.

The distinction between rules and learned patterns is critical. Learned patterns are things the system has observed and proposed based on experience. They go through a confirmation process before being trusted. Rules are things you have explicitly stated. They are permanent, they override learned patterns when there is a conflict, and they cannot be modified by the AI. Only you can change rules.

Writing Effective Rules

The best rules are specific, actionable, and testable. "Be careful with customer data" is a guideline, not a rule. "Never include customer email addresses, phone numbers, or account numbers in any content published to the website" is a rule that agents can follow precisely.

Rules should also explain why they exist when the reason is not obvious. "Do not use competitor names in advertising copy because our legal team has flagged trademark risks" gives agents enough context to apply the spirit of the rule in edge cases, not just the letter of it.

Avoid writing rules that are so broad they constrain useful behavior. "Never make assumptions" would paralyze an agent that needs to make reasonable inferences as part of its work. "Never assume customer intent; always verify by asking a clarifying question before recommending a product" is specific enough to be useful without being so broad that it prevents the agent from functioning.

Rules vs Governance

Rules are one component of the broader AI governance framework. Governance also includes confidence gating (preventing agents from acting on low-confidence decisions), audit trails (recording what agents did and why), approval workflows (routing certain decisions through human review), and monitoring (tracking system behavior over time). Rules define the boundaries. Governance is the entire system for ensuring those boundaries are maintained.

Updating Rules as the System Matures

Your rules will evolve as you learn how the system operates. Early rules tend to be conservative, restricting agent behavior significantly while you build trust. As you gain confidence in the system's judgment, you can relax some rules and tighten others based on actual experience. The key is reviewing rule effectiveness periodically: are agents being blocked by rules that are too strict? Are there situations where you wish a rule existed? Adjust accordingly.