What is the difference between agentic AI and generative AI?
Generative AI creates content, such as text, images or code, in response to a prompt, and then stops. Agentic AI uses AI models to pursue a goal: it plans the steps, uses tools and business systems, checks the results and acts until the task is done. Generative AI produces an answer; agentic AI completes the work.
- Generative AI is a capability; agentic AI is a way of using it. Most agentic systems use a generative model as their reasoning engine.
- The difference is action. An agentic system can book an appointment, update a record or resolve a query in your systems, not only draft text about it.
- Agentic AI needs guardrails. Because it acts, it needs defined tools, step limits, audit logs and human approval for high-risk actions.
- Start where the work is repetitive and rule-bound. Call handling, policy questions and reconciliation are proven starting points.
Agentic AI vs generative AI: a side-by-side comparison
| Generative AI | Agentic AI | |
|---|---|---|
| Main job | Create content | Complete a goal |
| Starts with | A prompt from a person | A goal, an event or a trigger (a call, an email, a new record) |
| Ends with | Text, an image or code for a person to use | A finished task: a booking made, a case resolved, a record updated |
| Uses tools and systems | Rarely | Yes: APIs, databases, calendars, CRMs |
| Number of steps | One response | Many steps, planned and checked |
| Main risk | Wrong or invented content | Wrong action in a live system |
| Example | Drafting a reply to a patient's email | Answering the patient's call and booking the appointment in the clinic's live diary |
What is generative AI?
Generative AI is AI that produces new content. Large language models (LLMs) such as GPT, Claude, Gemini and Llama generate text; other models generate images, audio or code. A person writes a prompt, the model returns content, and the person decides what to do with it. Generative AI is excellent for drafting, summarising and translating, but on its own it does not change anything in a business system.
What is agentic AI?
Agentic AI is AI that acts toward a goal with limited human direction. An agentic system breaks a goal into steps, chooses which tool or system to use for each step, reads the result and decides what to do next. When the work is a business process, such as resolving a query or processing a request from start to finish, this is called agentic automation.
Agentic AI vs AI agents: are they the same thing?
They are closely related. An AI agent is one software unit that perceives, decides and acts, for example a voice agent that answers calls. Agentic AI is the broader approach: one or more agents, the tools they may use, and the rules that govern them, working together toward a goal. In practice, "agentic AI" describes the system and "AI agent" describes its parts. Teknoloje builds both as custom AI agents fitted to each client's systems.
Agentic automation vs RPA: what changes?
Robotic process automation (RPA) follows fixed scripts: click here, copy this field, paste it there. RPA is reliable while the inputs never change, and it breaks when a screen, a document format or a customer's wording changes. Agentic automation reads unstructured input, such as a spoken request, an email or a PDF, and decides the next step. The best production systems combine the two: an AI agent understands the request, and deterministic, rule-based code executes the high-risk steps.
| RPA | Agentic automation | |
|---|---|---|
| Handles | Structured, predictable input | Unstructured input: speech, emails, documents |
| When something changes | The script breaks | The agent adapts within its rules |
| Best for | Stable, high-volume data entry | Conversations, judgement within policy, multi-system processes |
What are real agentic AI use cases?
These are agentic systems Teknoloje has in production, each with its measured result:
Healthcare: an AI receptionist that books into the live diary
A UK physiotherapy clinic replaced evening and weekend voicemail with a voice AI receptionist. The agent answers the call, checks real clinician availability, books, reschedules or cancels, and sends an SMS confirmation. Since go-live it has answered 100% of after-hours calls with zero missed bookings. (Read the case study; see also our AI receptionist for medical offices.)
Healthcare: call deflection at a medical center
A multilingual voice AI agent connected to a medical center's EMR now resolves routine calls about appointments, refills and lab results. It deflects 60% of inbound call volume and holds a 94% patient satisfaction score. (Read the case study.)
Banking: policy answers in seconds
At an enterprise bank, an agentic knowledge platform answers staff questions from policies and circulars, with every answer cited to its source. Staff spend 80% less time searching, and 70% of routine queries are resolved without a human. (Read the case study.)
Sales: calling new leads in under two minutes
For a solar installer, an outbound voice agent calls each new web lead in under two minutes, qualifies it and logs the result in the CRM, replacing next-day callbacks. (Read the case study.)
How is agentic AI used in finance?
Finance and banking teams handle large volumes of rule-bound work: matching transactions, answering policy questions, following up on settlements and checking compliance updates. Agentic AI fits this work because each step can be checked against a rule and logged. Typical uses are automated reconciliation across channels, customer-support agents that look up transaction status, and compliance assistants that answer questions from current circulars. Our FinTech AI solutions cover these use cases for banks, payment companies and FinTechs.
When should you use generative AI, and when agentic AI?
- Use generative AI when a person stays in control of every output: drafting, summarising, translating, brainstorming.
- Use agentic AI when the same multi-step task repeats many times a day and the steps can be described as rules: answering and booking calls, resolving standard queries, processing requests across systems.
- Keep a human in the loop for high-risk actions such as payments, clinical decisions or record deletions. An agent can prepare the action; a person approves it.
What are the risks of agentic AI?
Because agentic AI acts in live systems, a mistake has consequences. The four failures we see most often are agents that loop without limits, answers not grounded in company documents, sensitive data sent to outside services, and missing human approval for high-stakes actions. Our analysis of why agentic AI projects get cancelled explains how to design against each one, and our guide to on-premise LLM vs cloud covers keeping data inside your network.
Frequently asked questions
Is ChatGPT generative AI or agentic AI?
ChatGPT is mainly generative AI: it produces text in response to prompts. When it is given tools and allowed to take several steps on its own, such as browsing or running code to finish a task, it is working in an agentic way. The same model can power both.
Is agentic AI a type of generative AI?
Agentic AI usually uses generative AI as its reasoning engine, but it is a different way of using it. Generative AI returns content to a person; agentic AI plans steps, uses tools and business systems, and acts until a goal is reached.
What is the difference between agentic AI and AI agents?
An AI agent is a single software unit that perceives, decides and acts, such as a voice agent that answers calls. Agentic AI is the broader system: one or more agents, the tools they can use and the rules that govern them, working toward a goal.
Will agentic AI replace RPA?
Agentic AI replaces RPA where the input is unstructured or changes often, such as phone calls, emails and documents. For stable, structured data entry, rule-based automation remains reliable and cheaper. Most production systems combine an AI agent that understands the request with deterministic code that executes high-risk steps.
How long does it take to deploy an agentic AI system?
Teknoloje's typical go-live is six to ten weeks from signed contract to a live system in the client's environment. A simple voice AI agent can be piloted in under ten days; complex multi-system integrations sit at the longer end of the range.