Agentic AI vs. generative AI
Agentic AI vs. generative AI is a question of action versus output. One produces content and insights on demand; the other sets its own course, makes decisions, and carries out tasks to achieve a goal.
Agentic AI vs. generative AI is a question of action versus output. One produces content and insights on demand; the other sets its own course, makes decisions, and carries out tasks to achieve a goal.
For businesses, the distinction between agentic AI and generative AI is less about what’s under the hood and more about how each contributes to getting work done. Generative AI produces content and context when prompted. Agentic AI takes on a broader role, using that output to guide decisions and complete tasks. Learning a bit more about how they differ helps teams apply these complementary technologies in increasingly powerful ways.
Both agentic and generative AI (GenAI) share the capacity to evolve and learn from data and experience. And while they are often used together, they serve different purposes and have a few key distinctions:
Generative AI takes inputs (or prompts) and generates something new, such as text, images, or even a block of code. GenAI models are trained on massive datasets and learn patterns that let them respond in often surprisingly relevant ways. They’re great at things like summarizing reports, writing blocks of copy, or creating visual layouts.
AI agents typically use generative AI, but they take things a step further. Instead of having to wait for a prompt, they can autonomously decide what to do next based upon established goals. They observe what’s happening, plan a response, take action, and learn from the outcome – all in a loop. This makes them useful for automating processes, coordinating systems, or solving problems without constant human input.
Agentic AI refers to the framework achieved when multiple AI agents are integrated and coordinated within a single system or platform, operating toward an established goal. Agentic AI connects tools, data, reasoning, and action. And does so not just within one task, but across many. An agentic AI framework lets you guide agents, call on generative tools, trigger workflows, and keep things aligned to a broader objective.
So, in a nutshell: GenAI generates and agentic AI acts. GenAI can take all your research and meeting notes from a product launch planning session, for example. It can summarize them, organize the information, and suggest possible next steps. Whereas agentic AI can take those steps, assign owners, update systems, follow up on progress, and adjust the plan as conditions change.
| Generative AI | Agentic AI | |
|---|---|---|
| Primary role | Generate content | Take action toward goals |
| Input type | Prompt-based | Goal- or context-based |
| Autonomy | Reactive | Autonomous |
| Output | Text, images, code, audio, video | Actions, decisions, workflows |
| Planning capability | None – single step | Yes – multi-step planning and orchestration |
| Memory and learning | Limited memory, no ongoing feedback loop | Learns from outcomes and adjusts over time |
| Use of tools or APIs | May generate code for tools | Can trigger tools, APIs, and systems directly |
| Human involvement | Requires a user prompt for each task | Can operate independently, with oversight |
| Common use cases | Writing content, designing images, answering questions | Running processes, coordinating agents, acting on data |
| Relationship to each other | May be used by agentic AI | May use generative AI within the system |
Not every task needs decision-making. And not every task needs content creation. Most modern businesses that use AI agents also use GenAI. And even when they operate inside the same system, each has its own operational wheelhouse. Here is a handful of examples that illustrate the typical uses for each technology:
What generative AI is typically used for:
What agentic AI is typically used for:
AI technologies use a neural network modeled on how an actual human brain works. Their capacity for learning and adapting is practically limitless. For industries, this means that AI agents and models can quickly be taught sector-specific processes, workflows, and business logic – and continue to evolve and specialize indefinitely.
A&D programs are highly regulated. Teams must juggle contract flow downs, project pegged costs, and exceptionally strict and complex security and compliance obligations.
Auto supply chains are complex and multi-tiered. Teams must meet just-in-time sequencing requirements while adapting to the rise of EVs, autonomous technologies, and connected systems.
Speed and accuracy are non-negotiable in the food and beverage sector. Formula and labeling compliance, end-to-end traceability, and shelf life constraints are all essential, daily priorities.
From sketch to shelf, the fashion business depends on fast decisions and quick pivots – taking seasonal calendars, global sourcing, sustainability, and traceability into consideration.
Pressure is high with healthcare challenges, including ongoing staffing shortages, limited resources and budgets, and issues with data sharing and medical supply coordination.
Distribution margins are tighter than ever. Today’s distributors rely on accurate inventory data and fast response to keep delivery promises and run efficient distribution centers.
Machine learning is the core technology that powers both agentic and generative AI. It allows AI systems to detect patterns, make predictions, and improve their outputs through training. Generative models typically have deep learning architectures. This gives them the ability to make sense of context, process vast amounts of data, and generate whatever range of outputs they’re trained to produce (text, images, code). Agentic AI incorporates these same models and has these same capabilities. But it is also structured around what is called a goal-driven loop. This includes components for perception, planning, action, and feedback – allowing it to operate continuously and adapt to changing conditions. Not just to interpret data and the sky’s the limit as to what steps AI agents will increasingly be authorized to take. As we’ve all seen, because of its capacity to learn and tweak itself, AI evolves at an exponential rate. This means AI agents will likely be leaps and bounds ahead of where they are today in a matter of a few years – or even months.
Whether generative or agentic, all AI systems have the power to transform and reinvent everyday operations. But with that power comes a necessary commitment to use care and diligence when adopting and using these tools. Fortunately, today’s best AI-powered solutions are built with security and transparency in mind, so there will always be support and guidance as you set up your ongoing AI protocols.
Shifts in customer behaviors, supply patterns, or market factors can mean that the real world no longer aligns with your model’s original training data. To stay current, models need a retraining schedule, using fresh, high-quality data that reflects current conditions.
Most training biases are unintentional, but they proliferate nonetheless and can damage your brand and reputation. Reduce bias risks by using diverse datasets, testing for fairness, and applying filters that correct for skewed patterns.
Because AI models often accept natural language or open-ended inputs, they can be targeted in unexpected ways. It’s important that your IT team has proven, up-to-date experience and capability for AI-specific security threats and protocols.
“Black box” outputs are those for which the rationale or logic is not (or cannot be) explained to human users. Always require audit trails, decision logs, and override options. Ensure your system is set up with a “show your work” command.
Because of its efficiency and accuracy, it’s tempting to give agentic AI too much leeway. Place human-in-the-loop approval stages on all important tasks. This avoids errors and ensures your human teams stay on the ball and keep their edge.
For all their similarities and differences, the real takeaway is how tools like agentic and GenAI are revolutionizing the world of work and business. AI-powered solutions let us process, understand, and produce more information than ever before. And with agentic AI in the mix, companies are increasingly able to put that information to work – automatically, adaptively, and without even needing to ask. Today’s powerful AI innovations mark a shift from digital tools that react to our commands to systems that respond to their environments and learn. It’s a turning point not just in how software is built, but in how work gets done.
Learn how Infor GenAI, now embedded in Infor CloudSuites, can empower your users to be hyper-productive in everything they do.
See how Infor Industry AI Agents work directly in the Infor GenAI Assistant to help you orchestrate and automate complex, industry-specific processes.