Customer Service Automation Tools Comparison: Chatbots vs LLMs vs Agentic AI

→ Quick to set up

→ Handles FAQs and basic queries

→ Low cost

→ Cannot handle complex or multi-step workflows

→ Struggles with context if a conversation veers off script

FAQ pages, simple ticket routing, initial triage, etc.

A chatbot can answer “What’s your return policy?” but may fail if the customer asks, “I bought two items, one is damaged, can I return them separately?”

→ Natural, flexible responses

→ Understands context better than rule-based bots

→ Can summarize tickets, draft emails, and provide guidance

→ Can hallucinate or give inaccurate answers

→ Limited ability to perform multi-step tasks across systems

Complex inquiries, drafting messages, internal support guidance, etc.

An LLM assistant can explain a return policy in multiple ways and summarize a customer’s issue for the support team.

→ Process refunds and returns automatically

→ Update CRM and order management systems

→ Handle multiple tasks in parallel

→ Retain context across conversations

→ Faster resolution times

→ Consistent and accurate operations

→ Lower human intervention

With Azeon, an agent can check inventory, process a refund, update Shopify or Salesforce, and notify the customer, all without a human clicking buttons.

Pick a repeatable, rule-driven process that involves multiple steps or systems, like refunds, order updates, or support ticket triage.

Azeon’s Agent Library offers ready-to-go agents for common support workflows. These agents are designed to handle typical scenarios efficiently while giving you a starting point for customization.

Using Azeon’s visual builder, map your workflow, connect your systems, set decision rules, and add human-in-the-loop checkpoints as needed.

Run the agent in a controlled environment, simulate real requests, and ensure it behaves as expected across systems.

Start with a single channel or workflow, monitor performance, and scale to additional tasks and regions.

Use Azeon’s analytics dashboards to track resolution times, errors, and customer satisfaction, refining workflows as needed.

LLMs can understand free-form customer questions and draft responses. Agentic AI takes that context and acts on it, like pulling order info, issuing refunds, or logging updates in your systems. They complement each other nicely.

Not really. Platforms like Azeon are built with integrations in mind – CRM, Shopify, Salesforce, helpdesk tools – you name it. You pick an agent, plug in the systems, and it can start working quickly.

It’s way safer. LLMs generate text, which sometimes can be wrong. Agentic AI focuses on actions and workflows, so it executes specific steps, reducing human-like “hallucinations.” Guardrails and monitoring make it even safer.

Yes. Most agentic AI platforms, like Azeon, let you customize agents to your workflows. You define the steps and rules, and the agent learns how to follow them reliably.

Fast. You can automate a single workflow, like refunds or ticket summaries, in a few days, not months. Then scale up as your team and system confidence grow.

Agentic AI: Autonomous AI that can execute multi-step workflows across systems, not just respond to messages. Example: processing refunds, updating CRM, and notifying customers automatically.

Azeon: An enterprise platform for building and managing agentic AI. Offers prebuilt agents, integrations with business tools, and a low-code environment for workflow automation.

Chatbot: A rule-based automated system that responds to customer messages using predefined scripts or keywords. Best for FAQs or simple interactions.

LLM (Large Language Model): AI models trained on massive amounts of text data that can generate human-like language and understand context. Examples include GPT-4.

Multi-step Workflow: A sequence of automated actions performed across systems to complete a task, like refunding an order, updating a database, and notifying the customer.