AI Agent is an artificial intelligence system capable of performing a series of tasks on its own to complete a goal: reading requests, looking up data, deciding on the next step, taking action, and only asking people when needed. Unlike traditional chatbots that only answer questions, AI Agent can also do the job. For example, in customer service, an AI Agent not only explains the return policy but also generates return slips, updates order status, and sends notifications to customers in accordance with business processes. Zenify notes that Lumi AI Agent processes an average of 74% of level 1 requests on customer systems, with the remainder going to employees.
TL;DR
- AI Agent = AI that "knows how to do", automatically runs a series of tasks from start to finish; chatbot = "talking" AI, which only answers.
- Different from chatbots in 3 ways: has context memory, has tools/actions, has multi-step reasoning chain.
- In customer service, AI Agent automatically handles repeat requests 24/7, helping the team focus on human needs.
- Zenify: Lumi AI Agent handles ~74% of level 1 tickets on its own, only transferring the parts that need intervention.
Why is AI Agent different from traditional chatbots?
Because their goals are different: chatbots are designed to answer, AI Agents are designed to get things done.
Features | Traditional Chatbot | AI Agent (Agentic AI)
Answer questions | ✅ | ✅
Perform tasks yourself (create tickets, update orders, send emails) | ❌ | ✅
Long contextual memory | Limitations | ✅
Multi-step inference to achieve the goal | ❌ | ✅
Decide for yourself when to ask someone ❌ | ✅
Learning improves with feedback | Very little | Can
Chatbot replied "return policy is 7 days". AI Agent responds and then executes it: checks the return deadline, creates a request, updates the warehouse team, sends a confirmation to the customer, and notes it in the file.
What are "Agentic AI" and "AI Copilot"?
Agentic AI is a branch of AI capable of planning and executing actions autonomously within a permitted range. This phrase is often used to describe the new generation of AI, including AI Agents. AI Copilot works in "sitting next to" the employee: suggests answers, summarizing conversations, suggests next steps, but humans are still the final decision makers.
In actual customer service operations, the two modes complement each other:
AI Agent (autonomous): automatically handles recurring requests (look up order status, respond to business hours, update basic information), runs 24/7 without human intervention.
AI Copilot (assisted): Supports staff during difficult conversations: summarizes calls, suggests scenarios, reminds SLA is about to expire.
Data: On the Zenify system, the processing productivity of employees supported by AI Copilot increased on average +37% compared to manual operations (public data on zenify.vn, copyright confirmation required).
What tasks can AI Agent handle in customer service?
The scope of AI Agent's self-processing is usually requests with clear procedures and few exceptions, i.e. "level 1 tickets".
Answer repeat questions: price, business hours, return policy, how to use.
Look up status: Where is the order, who is handling the ticket?
Update basic information: change address, update phone number according to granted rights.
Gather information: ask more questions to get enough context before transferring people.
Classification & routing: read intent, label, route to right flow/right person.
Create internal requests: open tickets, make appointments, CRM notes.
When encountering a request that goes beyond the scope (complex complaint, customer needs in-depth consultation), AI Agent proactively hands over to the staff with full context, this is the key point to not upset the customer.
Can AI Agent replace employees?
Do not replace the whole thing, but change the work structure: AI Agent takes on the repetitive workload, employees focus on the parts that need thinking and emotions. The common model is "AI handles the long tail, humans handle the peak".
On the Zenify system, Lumi AI Agent processes 74% of level 1 requests on its own. That means the team is not replaced but is freed up ~¾ of the repetition volume for consulting, closing orders, and handling complex issues. For growing businesses, the practical result is processing more messages without increasing headcount, which is different from "mass layoffs" as many people fear.
Deploying AI Agent: where to start?
The safest way to deploy is to start small on one channel, clearly define the scope, and always keep "human-in-the-loop" mode.
Select 1 channel + 1 group of repeat requests (for example: ask for price + working hours on Zalo).
Knowledge base level: AI needs standard information sources to answer correctly (price, policy, process).
Definition of scope and handover rules: Which questions are answered by oneself, which questions are asked by someone, according to corporate policy.
Monitoring by AI QA: scores 100% of conversations processed by AI to detect standard deviations.
2-week measurement: containment rate (self-processing rate), CSAT of conversations by AI, correct handover rate.
If you want to go through each step in detail, see the article AI Agent handles 60% of level 1 tickets: deployment scenario and 90-day roadmap for deploying AI for customer service.
How to avoid AI Agent "fabricating" information?
The way to prevent AI hallucination is to limit the source of responses to enterprise-controlled data (RAG technique), prohibit AI from inferring outside the scope, and automatically score every conversation. specific:
AI only answers based on the knowledge base updated by the business, not based on general knowledge.
Sentence out of range → return default sentence + hand over person, no guessing.
Auto-QA scores 100% of conversations according to business standards, detects errors for immediate correction.