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 agentic AI platform for customer experience, 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.
Frequently asked questions
- Where does business logic live in an AI agent?
- In the instructions and conditions that govern its actions. In Aide it lives in ASOPs, each scoped to a single classified intent, so the logic is organized by what the customer actually wants.
- How is business logic for an AI agent tested?
- By running the agent against real past conversations before deployment, so you see how the encoded logic behaves on actual cases, not just how it reads.