A traditional chatbot follows a scripted conversation tree and returns information. An AI agent works towards a goal: it can break a request into steps, look up what it needs across your systems, call APIs to perform actions such as creating a ticket or updating a record, check its own output, and escalate to a person when the situation falls outside what it is permitted to handle.

• Scoped access — the agent gets its own credentials with the narrowest permissions the task allows, never blanket admin access
• Read before write — most deployments start read-only, with write actions added once accuracy is proven
• Approval gates — financially or contractually significant actions route to a person before execution
• Full audit trail — every action logged and reversible, so behaviour can be reviewed after the fact

• Grounding — responses are drawn from your documented sources through retrieval-augmented AI rather than from the model's general knowledge
• Citations — answers reference the source document so users can verify them
• Defined boundaries — the agent is instructed to decline and escalate outside its scope instead of guessing
• Evaluation sets — accuracy is measured against a curated set of real questions before go-live and monitored afterwards
• No system is perfect — which is why we scope agents to work where an occasional escalation is acceptable