Agentic AI vs generative AI: The real difference and when to use each

Categories: Business Insights Date 22-Jun-2025 5 minute to read

Artificial intelligence sits in almost every conversation right now. Everyone wants to understand what’s possible, what’s hype, and where the real value hides. But the moment you try to compare agentic AI vs generative AI, the explanations usually drift into jargon or big promises that don’t help anyone make an actual decision.

So let’s put it simply.

Generative AI helps you create things: words, images, ideas, code.
Agentic AI helps you get things done.

One supports your thinking.
The other moves work forward.

And once you see the difference between agentic and generative AI in real examples, the whole picture becomes clearer. You start to understand why teams that already use gen AI now ask how to take the next step: how to move from “AI that drafts” to “AI that acts”.

What is gen AI?

Generative AI focuses on producing content. It takes huge amounts of training data and uses it to create text, images, ideas, and code.

If you need a document rewritten, notes summarised, a proposal drafted, or a set of ideas to kick-start a meeting, generative AI handles that fast and consistently.

Sales teams use it to prepare call summaries and emails. Developers generate test cases or repetitive setup code that usually takes time to write by hand. Product teams turn messy notes into something clean and structured.

It speeds up thinking-heavy work, but it stays inside the boundaries of the prompt you give it.

How can generative AI be used in the payments industry?

Strictly speaking, the use of Artificial Intelligence (AI) in business – and the payments industry in particular – is old news.

What is agentic AI?

Agentic AI goes further. These systems take a goal, break it into steps, choose the right tools, take action, and adjust when the result isn’t right.

Think of it as a digital teammate that plans, coordinates, and executes work.

An underwriting AI agent can receive a submission, check what’s missing, request documents, run a rating model, and prepare a first-pass quote. A logistics AI agent can update delivery routes in real time. An engineering AI agent can create a branch, update dependencies, run checks, and open a pull request.

This isn’t AI that answers questions. It’s AI that gets work done.

Digital transformation: How agentic AI is redefining enterprise growth

The real value of agentic AI lies in its ability to drive autonomous decision-making, but businesses must capture that value with clear guardrails to manage risk.

How agentic AI works

You describe the outcome you want, not the steps. The AI agent builds a plan, gathers what it needs, checks formats, calls tools, runs actions, and evaluates the result.

It doesn’t stay locked inside a chat window. It interacts with your CRM, spreadsheets, databases, APIs, and external systems.

And if something breaks, which always happens, the agent doesn’t stop. It retries, asks for context, changes direction, or adjusts the plan.

Over time, it learns what works best. That’s how it shifts from “assist me” to “work alongside me”.

Real agentic AI vs generative AI Examples

Insurance

Generative AI helps with content: policy summaries, explanations, broker drafts.
Agentic AI in insurance handles the workflow: reading submissions, checking completeness, running models, and preparing quotes.

Healthcare

Generative AI simplifies lab results and clinical notes.
Agentic AI for healthcare validates formats, updates EHRs, schedules follow-ups, and coordinates data across systems.

Logistics

Generative AI helps with communication and documentation.
Agentic AI adjusts routes dynamically based on demand, conditions, and constraints.

Software engineering

Generative AI writes snippets, documentation, or test ideas. Agentic AI triages issues, generates patches, updates dependencies, and raises PRs.

Benefits of agentic AI

Agentic AI shines in workflows where people lose time to repetitive steps and constant switching between tools. It speeds up processes, reduces errors, keeps execution consistent, and gives teams more time for work that actually requires judgement.

It removes the admin but not the expertise.

Where generative AI still leads
Not every problem needs an autonomous agent. When you need ideation, creativity, drafting, rewriting, early research, or visual output, generative AI stays the best choice. It delivers speed without requiring complex setup.

Agentic AI only makes sense when you need the work done, not just described.

Common misconceptions about agentic AI

Some fear agentic AI replaces full teams. It doesn’t. It replaces repetitive operational tasks.

Others think generative AI is now outdated, but generative models actually power many agentic systems.

And “agentic AI = automation with buzzwords” isn’t correct either. Traditional automation breaks when something unexpected happens. Agentic AI adapts.

What you need before you build agentic AI

Before you build agentic AI, you don’t need perfect data or a new architecture, just a foundation solid enough for an AI agent to operate without stumbling over gaps. Your data needs to be reachable, your systems have to expose real actions (not just information), and the workflow itself should be stable enough for an agent to follow without guessing.

You also need clear boundaries for what the agent can do independently and when it should pause and ask. Most organisations can’t align all of this on their own, so having a technical partner who understands integrations, LLM behaviour, and operational guardrails often determines whether an agent works reliably in production or stays stuck at the demo stage. Once these basics are in place, start with one painful workflow that can show value quickly.