Agentic workflow

Agentic AI is customer service software that plans and executes multi-step actions toward a resolution. It looks up an order, issues a refund, or updates a record, rather than only generating a reply. This sets it apart from a rules-based chatbot, which follows a fixed decision tree. It also differs from conversational AI, which understands natural language but stops short of taking action. The defining test is whether the system can complete a transaction in a system of record, not how natural its responses sound. For well-scoped inquiry types, agentic AI customer service tools are reported to handle 60–80% of requests autonomously. OnClarity’s AI Support Agent operates in that range across email, chat, WhatsApp, and voice.

AI agent orchestration is the control layer that decides which agent or model handles a task, in what order, and when to hand off to a human. A single agentic workflow describes one agent’s path through a task. Orchestration coordinates multiple agents and tools. It uses supervisor or router patterns to direct work, pass context between steps, and log each decision for audit. For example, within OnClarity’s omnichannel inbox, a triage step could classify a billing query and hand it to a specialized agent with full conversation context intact, escalating to a human when a dispute crosses a defined threshold.

An AI agent is software that resolves a customer request end to end, taking actions in connected systems rather than only answering questions. Unlike a scripted chatbot, an AI agent can look up an order, issue a refund, or update a record, then confirm the completed outcome back to the customer.

Human in the loop describes a review step where a person approves or corrects AI output before it reaches the customer. Teams typically apply it to high-risk intents such as refunds, cancellations, and complaints, then relax it selectively as measured accuracy on those intents proves out.

Workflow automation chains system actions — issue a refund, reissue a document, update a record — so a resolution completes itself without an agent performing each step. It separates an AI agent that resolves requests from a chatbot that can only describe how a request would be resolved.