An agentic workflow is a multi-step process in which an AI model plans steps, calls tools, and checks results to complete a task. In customer service, it is the sequence an AI agent runs to complete a request, such as a refund, an address change, or a claim.
The term is used loosely. In Building effective agents (December 2024), Anthropic describes workflows as "systems where LLMs and tools are orchestrated through predefined code paths." In agents, by its definition, "LLMs dynamically direct their own processes and tool usage." Vendors use "agentic workflow" for either one, and for mixes of the two, so it helps to ask which parts follow a fixed path.
How an agentic workflow works
A typical customer service workflow repeats a loop of five steps until the request is done.
Understand. The model reads the message or the call transcript, works out the intent, and picks out details such as an order number.
Plan. It decides the next step: ask a question, look something up, or act.
Act. It calls a tool, such as the order system, the CRM, or a payment API.
Check. It compares the result with the goal and the business rules. If something is missing, it asks again or tries another path.
Close or hand over. It confirms the outcome with the customer, or passes the case to a person with a summary.
In a well-built workflow, each step leaves a record of what the model saw, what it called, and what came back. Teams use that record to test the workflow and to audit it later.
Common agentic workflow patterns
The Anthropic post names five patterns. Four of them come up often in customer service.
Prompt chaining. Each model call works on the output of the previous one. For example, the system summarizes a complaint, classifies it, and then drafts a reply.
Routing. The system classifies a request and sends it to a specialized follow-up task. A billing question and a technical fault go down different paths. When several agents are involved, an orchestrator does this routing.
Orchestrator-workers. A central model breaks a task into parts and hands each to a worker. A house move, for example, can touch the address, the delivery slot, and the contract at once.
Evaluator-optimizer. One model call produces an answer and another reviews it in a loop. Teams use it to check a draft reply against policy before it is sent.
The same post recommends "finding the simplest solution possible, and only increasing complexity when needed." In a contact center, that often means fixed paths for regulated steps and more model freedom in the conversation around them.
Agentic workflows in the contact center
Teams usually decide, step by step, what the model may choose and what must run the same way every time.
Telecom. A customer asks to change plans. The model handles the conversation and explains the options. Eligibility, the new price, and the contract terms come from the billing system and fixed rules.
Banking. A customer reports a lost card. The identity check and the card block follow a fixed sequence, and the model explains the next steps and answers follow-up questions.
Ecommerce. A return passes through order lookup, a policy check, a shipping label, and a refund. Each check can be a deterministic step, built for example as flows, while the model keeps the conversation natural.
Logic that lives only in a prompt can behave differently from one conversation to the next. For amounts, disclosures, and eligibility, many teams keep the decision in code or rules and let the model handle the language.
Frequently asked questions
What is the difference between an agentic workflow and an AI agent?
An AI agent is the system that talks to the customer and takes actions. An agentic workflow is the sequence of steps the agent runs to finish one task. One agent can run many workflows, and one workflow can involve several agents.
What is an example of an agentic workflow in customer service?
A delivery change is a common one. The agent identifies the customer, finds the parcel, checks which slots are free, books the chosen slot, and confirms it. If the carrier's system rejects the change, the agent explains why and offers another option.
How is an agentic workflow different from RPA or a scripted chatbot?
RPA and scripted bots follow paths defined in advance, click by click or turn by turn. An agentic workflow lets a language model interpret free-form requests and choose among steps. Many production systems combine both, with model-driven conversation and fixed paths for the steps that carry risk.
Are agentic workflows safe for regulated processes?
They can be, when the regulated steps are fixed and logged. Identity checks, required disclosures, and amounts are often kept outside the model's discretion. It also helps to test every change against a set of conversations before release (see AI agent evaluation).
How we approach this at Zowie
Zowie builds AI agents for voice, chat, and email. In each conversation, the language model speaks. A separate, deterministic Decision Engine returns every amount, rate, offer, disclosure, and escalation from your own rules, with an audit log. You are welcome to book a demo.
Explore: Agentic AI, Orchestrator, Flows, AI agent, AgentOps, AI agent evaluation