Plain definitions: content, action and the system doing it
Generative AI creates content from an instruction and available context. This might be a reply, a summary, an image or code. The result is something a person can read, review or use. OpenAI offers ChatGPT, Anthropic offers Claude, and Google DeepMind develops Gemini models. These are examples of models and assistants, not complete descriptions of each product.
Agentic AI describes an approach in which a system pursues a goal by choosing and taking actions. An AI agent is the system doing that work. It may use a generative model, tools, stored context and rules. The key question is not whether it can write a convincing answer, but whether it can take authorised steps towards an outcome. For more background, read what is an AI agent.
The core difference between agentic AI and generative AI
The difference between agentic AI and generative AI is clearest at the handover point. A generative workflow might produce a customer reply for a person to send. An agentic workflow might retrieve relevant information, prepare the reply and send it if its permissions allow. Both can involve generated text, but the second also changes something outside the conversation.
Treat these as patterns of use, not fixed labels for every product. A conversational interface can sit in front of an agent, while a powerful model can remain limited to drafting. Tool access alone does not settle the distinction. Look at the goal, permitted actions, checks and the point at which a person must approve the work. These patterns do not guarantee capabilities for any particular supplier.
| Dimension | Generative AI pattern | Agentic AI pattern |
|---|---|---|
| Goal | Produce requested content | Pursue an assigned outcome |
| Output | An answer, draft, image or code | Actions, changed records and a result |
| Autonomy | Usually responds to an instruction | Chooses steps within defined limits |
| Tools | May use tools to support an answer | Uses tools to carry out work |
| Risks | Incorrect or unsuitable content | Incorrect content plus unintended actions |
| Examples | Draft a reply or summarise notes | Handle an authorised workflow |
| How you pay | Check access and model usage charges | Check model, tool and execution charges |
Agentic AI vs AI agents: approach versus implementation
Agentic AI vs AI agents is not a choice between competing technologies. Agentic AI describes the behaviour or approach: pursuing a goal through decisions and actions. AI agents are the systems that implement that approach. Agentic describes how work happens; agent identifies what carries it out.
Calling software an agent does not explain its permissions, reliability or scope. Ask what it can observe, which tools it can use, what it can change and when it stops. Salesforce describes Agentforce as an AI agent platform for employees and customers in the Salesforce ecosystem. Microsoft describes Copilot Studio as a way to create AI agents, workflows and apps. These descriptions explain their intended role, but a business must still assess its particular configuration. For the interface distinction, see AI agent vs chatbot.
How generative models and agents work together
Generative AI and agentic AI often work together rather than replace each other. A generative model inside an agent can interpret an instruction, suggest a next step or compose a message. The surrounding system manages tool access, context, execution and checks. Agentic AI vs genAI is therefore a difference in the overall workflow, not a contest between models.
Consider a request to update a customer record from an email. The model could extract the requested changes. The agent could check whether the record exists, apply permitted updates and report what happened. A person might approve sensitive changes before execution. The model handles interpretation and content; the agent system connects those outputs to actions. This is a possible design, not a verified feature of any named product. Good design separates suggested actions from permission to execute them.
Business examples: drafting versus completing a workflow
Side-by-side examples help teams identify what they need. If the task ends with a draft for someone to review, a generative workflow may be enough. If it requires authorised changes across business applications, an agentic workflow may be more suitable. These scenarios are illustrative designs, not capabilities that every named product supports.
Product descriptions offer useful starting points. Cognition describes Devin as an autonomous software engineer. Agentforce focuses on agents in the Salesforce ecosystem, while Copilot Studio supports creating agents, workflows and apps. Terabot (our product), by AITG (our company), provides AI digital workers, each with a private virtual machine, working in applications such as CRM, email, spreadsheets and web apps. These are different approaches to deploying agents. Choose based on the work and controls required. Do not assume all agent products operate in the same way.
| Business task | Generative workflow | Illustrative agentic workflow |
|---|---|---|
| Sales | Draft a follow-up email | Check a record and send an approved follow-up |
| Customer support | Suggest a reply | Retrieve case details and take permitted action |
| Back office | Summarise incoming information | Check information and update an authorised record |
| Software work | Generate a code suggestion | Work through a task using permitted development tools |
Risks and controls change when AI can act
Generative AI needs checks for accuracy, confidentiality and suitability. Agentic AI needs those checks too, plus controls over execution. An incorrect draft can be caught before use. An incorrect action may change a record, send a message or trigger further work. The distinction is about consequences. Neither approach is inherently safe.
Start with narrow permissions and a clear definition of success. Decide which actions require approval, what information the system may access and how work can be stopped. Keep a record of actions so people can investigate mistakes. Treat instructions found in emails, documents or web pages as untrusted input, not automatic authority. Test incomplete information and unexpected requests, not only straightforward cases. A useful pilot shows where the workflow succeeds and where it should pause for a person.
How to choose the right approach for your business
Choose generative AI when the main need is creating, explaining or summarising content and a person remains responsible for the next step. Consider agentic AI when the outcome requires actions, tool use and follow-through. Use both when a workflow needs generated content and controlled execution.
Before selecting software, write down the task, required inputs, permitted actions and completion criteria. Identify exceptions that must go to a person. Then assess products against that specification. Compare the full operating cost, including model use, execution, connected tools and human review where relevant. Do not assume a subscription includes every part of a workflow. Start with work whose outcome is easy to verify and whose mistakes are manageable. Expand permissions only after testing. Choose the arrangement that fits the task, not the category that sounds more advanced.
What global and Malaysian teams should check
The distinction between generative and agentic AI is the same for global and Malaysian businesses. Procurement questions depend on the organisation's data, applications and operating requirements. Ask where information is processed, which systems receive it, how access is controlled and whether the workflow meets your contractual obligations. Do not infer deployment arrangements from a supplier's name or office location.
Hosting and agent execution are separate questions. A local infrastructure option does not establish where every connected model or application processes information. Request a clear account of the complete workflow, including external tools and human access. For customer messaging, review consent and channel terms before enabling automated actions. For internal operations, check permissions against existing business roles. Apply these checks to content tools and agents, with additional scrutiny wherever the system can change records or communicate externally.
A next step for teams that need AI to do the work
Terabot (our product), by AITG (our company), is an option for businesses exploring AI agents that work in existing applications. Each digital worker gets its own computer, a private virtual machine, and works around the clock. Worker types include Sales, Support, Back office, Computer-use and WhatsApp workers. It is in private preview; businesses can request an invitation.
Its WhatsApp worker replies through WhatsApp Web on its own computer. It is not the official WhatsApp Business API and never sends bulk messages. WhatsApp's terms restrict automated use, so a WhatsApp Business number and opt-in contacts are recommended. Monthly plans have unlimited digital workers and differ by AI and computer usage; prices are announced at launch. Assess any workflow against your permissions and review requirements. Evaluate whether execution fits your needs; agents do not need to replace every content tool.
Last updated October 6, 2026