Generative AI creates text, images, audio or video on request, while agentic AI acts toward a goal, and that is the core of agentic AI vs generative AI. Agentic AI works over several steps, using tools and systems to act, such as checking a calendar and booking an appointment. One writes, the other does, and the controls differ.
The word "agent" now turns up in pitches from practice management vendors, phone system providers and cold emails promising to run your intake overnight. It gets applied to everything from a chat window to software that books appointments on its own, and those are very different purchases. Before you sign anything, you need to know which one is on the table.
I'm Nikhil Sharma, CEO and software architect at DigiBenders in Saint John. I've spent twelve years shipping software, starting as a healthcare software architect, where privacy, audit trails and data quality decided whether a system could go live at all. Our team builds both kinds of AI: a generative video workflow for one client and live AI receptionists for two law firms.
This matters now because the risk changes with the type of AI. A tool that drafts a letter and a tool that sends it need different controls, different owners and different questions for the vendor. Below you will find plain definitions, a side-by-side table, the spectrum between the two, examples from our own projects, the controls an agent needs and a short checklist for deciding where to start.
Generative AI writes or creates something when a person asks for it. Agentic AI is given a goal and works through the steps to reach it, using your other systems along the way.
Your team has probably already tried generative AI. Someone types a request, called a prompt, and a large language model returns a draft, such as a client letter, a summary of a long file, a social post or an image. IBM describes it as creating new content such as text, images, video, audio and software code. Nothing changes in your systems until a person takes that draft and uses it.
Agentic AI starts from an objective instead of a single request. The same explainer describes it as achieving goals by planning, making decisions and carrying out multi-step workflows with varying levels of autonomy. In practice, the AI calls tools. It reads a calendar, checks a CRM record, books a slot and sends a confirmation, and it decides the order as it goes.
If you need one line for a partners' meeting, use this one: generative AI drafts, agentic AI does.
Generative AI answers a request with content, and agentic AI pursues a goal with actions. The table shows the difference between agentic AI and generative AI in six rows that matter when you are deciding what to buy.
| Generative AI | Agentic AI | |
|---|---|---|
| What it does | Creates content when someone asks | Works toward a goal over several steps, using tools and systems |
| What starts it | A person's prompt | A goal, a trigger or an event, such as an incoming call |
| What it produces | Drafts such as text, images, audio or video | Actions such as a booking, a CRM entry or a sent message, plus any content needed along the way |
| A business example | Turning product photos into short videos with an AI voiceover, finished by an editor | An AI receptionist that answers a call, books the appointment and logs the enquiry |
| The main risk | Says the wrong thing, such as inaccurate or confidential content | Does the wrong thing, such as an action in a live system that may be hard to undo |
| Who supervises it | The person who reviews the output before it is used | A named owner who sets permissions, approves irreversible steps and reads the action log |
Thomson Reuters frames it for legal and professional readers: agentic AI makes decisions and takes action to keep a process going, while generative AI reacts to input and creates output. The same article says one isn't better than the other, and I agree. They solve different problems.
The last row matters most. With generative AI, supervision happens at the end, when someone reads the draft. With an agent, you have to design supervision before it starts. By the time anyone reads the log, the booking has been made or the message has gone.
No. A chatbot answers in a conversation, while an agent connects to other systems and acts. Between the two sit two middle rungs, and that middle is where I see most of the useful work happen.
MIT Sloan draws the same line. It contrasts familiar chatbots that field questions and solve problems with agentic systems that integrate with other software to complete tasks independently or with minimal human supervision. Sorting tools into two boxes hides the choice you are really making, which is how much freedom and access you give the software.
Think of it as four rungs:
In my experience, most of what gets sold to firms as an agent today is rung three with a little of rung four, kept inside fixed guardrails. I think that is usually the right design. A guided workflow behaves the same way every time, so you can test it, explain it to a client and audit it later. Give the AI freedom only at the one step where judgement helps, such as working out what a caller wants, and fix every other step in advance.
Our generative work produces media that a person finishes, and our agentic work takes actions inside a client's own systems. Here is one example of each from projects we run now.
We run an AI product video workflow for a client that turns product photos into 40 to 50 second multi-scene videos with an ElevenLabs voiceover. What comes out is a draft. A video editor finishes each one, which is where human review belongs in generative work. The workflow writes and assembles. It does not act anywhere else.
The AI receptionist we built for Costin & Associates in Saint John answers calls, books and confirms appointments and logs every enquiry in the firm's CRM. For CLG Injury Law, the receptionist handles after-hours enquiry capture across CLG Injury Law's six offices and notifies the team right away. Both run around the clock, connected to each firm's CRM.
Nobody types a prompt here. A call comes in, and the system works through several steps across the phone line, the calendar and the CRM to reach a goal: the enquiry is captured and, where the caller needs one, an appointment is booked. That is agentic AI in the useful sense.
Notice how narrow the job is. It covers one process, which is answering calls and getting enquiries into the CRM. I think that is the right shape for a first agent, and it passes the test I learned in healthcare software: every enquiry lands in a record the firm can check.
Generative AI can say the wrong thing, and agentic AI can do the wrong thing. Someone catches a wrong draft when they read it, but a wrong action has already happened by the time anyone looks, so an agent needs its controls built in before launch.
The generative risks are accuracy and confidentiality. Models can state things that are not true, which is often called hallucination. The Office of the Privacy Commissioner of Canada notes in its generative AI principles that the content is probabilistic and may vary even when the same prompt is used again. Confidentiality is the other risk, because client details pasted into a tool can end up somewhere you did not intend. You manage both with review and a clear policy on what goes into which tool.
An agent adds a third risk, which is the wrong action. Before an agent goes anywhere near client data, it needs five controls:
Those principles were published on December 7, 2023. They state that generative AI tools do not occupy a space outside current legislative frameworks. The page also notes that privacy authorities have called for strong guardrails so AI systems are safe, privacy protective, transparent and accountable. The principles were written for generative AI, but I read accountability and safeguards as applying just as directly to an agent, which reads personal information and then acts on it.
Start with generative help that a person reviews, then add one narrow agent on one process, such as answering calls. Four questions will tell you which one a given job needs.
The order is deliberate. Start with generative AI for business tasks where a person already reviews the output, such as first drafts of letters, summaries and marketing copy. Your team learns what the tools get wrong while the stakes are low. Then pick one process with clear steps and a clear owner (answering calls is a good candidate) and put one narrow agent on it, with the five controls above.
When you are ready to scope either step, start with our AI automation and consulting services. If you are also wondering how clients find you once they ask an AI assistant instead of searching, read how AI assistants are changing search.
Ask what it can touch, whether it can act without approval, where the logs are, what happens when it gets something wrong and where your data is stored. Specific answers are a good sign. Vague answers tell you the product is not ready for client data.
When a vendor pitches you an agent, read these five questions aloud on the call and write down the answers.
| Question to ask | What a good answer includes |
|---|---|
| What systems can it touch, and with what access? | A named list of systems, and which ones it can only read and which it can change |
| Can it act without human approval? | You choose which actions need approval, and anything irreversible needs it by default |
| Where are the logs? | Every action recorded with its time and record, readable and exportable by your team |
| What happens when it gets something wrong? | A handoff to a person, a way to reverse the action and a named support contact |
| Where is our data stored? | A stated location, how long it is kept and whether it is used to train models |
The second and third questions matter most. If the software can act without approval and you cannot change that, you are buying autonomy you have no way to control. If there is no log you can read, you cannot show anyone what the software did with a client's information, and the accountability still sits with your firm.
The real question is not agentic or generative AI. It is how much permission you give a system and what it records when it uses that permission. A tool that writes needs a reviewer; a tool that acts needs an owner, an approval gate and a record of every step. Start with generative help that your people review, add one narrow agent on one process that is written down, and hold every vendor to the five questions above before you sign. Before you buy either, work through our AI readiness self-assessment to see where your processes and data stand, and once you have your answers, book a free consultation with our team in Saint John.
