Short answer
Business logic for AI agents is the set of rules, policies, and conditions that govern what an agent is allowed to do: when a refund is approved, what data may be shared, which cases must escalate to a human, and how exceptions are handled. It is the company's operating policy, expressed in a form the agent follows.A language model is good at understanding requests but knows nothing about your refund window or your escalation thresholds. Business logic supplies that. The open question is how it is expressed: as brittle hand-coded rules, as one large natural-language instruction set, or as something scoped and testable per intent.
Aide, the AI platform for customer service, expresses business logic as ASOPs (Agentic SOPs): intent-scoped, condition-action instructions that apply after an intent is classified off the Customer Intent Map. Because each ASOP is bound to a single intent, the logic for refund requests is separate from the logic for shipping delays, which makes it readable, testable, and auditable on its own.
Because each ASOP is scoped and explicit, it can be proven before it matters. Every ASOP runs in the Agent Simulator against real historical conversations before it ships, and once live, each action the agent takes leaves a reviewable record. Plainly authored, intent-scoped logic also stays legible: the team owns its policies, can reason about them, and keeps extending them as new intents are mapped, rather than surrendering its operating knowledge to a monolithic black box.