Agentic AI vs. Generative AI: How do they compare to one another?

Two concepts are shaping modern AI: generative AI, which creates content from prompts and agentic AI, where systems work toward defined goals, make decisions and act without constant input. This guide explains how they differ and where each one works best.

What is generative AI?

Generative AI refers to AI systems that produce new content based on existing data. This includes language models like GPT-4, AI image generators like DALL-E and code tools like GitHub Copilot. The output is reactive, meaning the AI generates a result based on a prompt. What makes generative models so useful is how flexible they are. That said, they don’t have the ability to set goals or follow through on tasks on their own.

What is agentic AI?

Unlike purely generative systems, agentic systems break down tasks into smaller steps, plan how to complete them and adjust their approach as they go.

Here are some common examples of agentic systems:

• AutoGPT: Automatically breaks down a higher-level objective into subtasks, runs web searches and documents progress step by step.
• LangGraph: A framework for building agent workflows using state machines. It helps coordinate multi-step processes, including branching logic and tool use.
• ReAct agents: Solve tasks step by step by reasoning through problems and using external tools like search engines or calculators. They adjust their actions based on the results they get.
• Multi-agent systems: Use several specialised agents that work together, share information and divide up tasks to solve more complex problems.

Agentic systems can also use APIs, data sources and external tools to pull in information. This lets them make decisions and complete tasks independently, from the initial goal all the way through to the final result.

How to compare agentic AI vs. generative AI

What are the strengths and weaknesses of agentic AI and generative AI?

Choosing the right type of AI depends on what kind of tasks it needs to perform. Generative AI and agentic AI are built on different principles, and each comes with its own strengths and weaknesses.

Generative AI in detail

Generative AI works best for tasks where the output depends on a specific user input. It can generate content fast and adapt it to different formats. The results are also generally high in quality.

Advantages of generative AI:

• Delivers content in seconds: Whether it’s text, images, or code, generative AI produces output almost instantly
• Easy to scale up: Generative models can be deployed quickly and used by many people at once without extra setup
• Easy to use and control: Just describe what you want using prompts
• Fits into existing processes: It’s widely used in marketing, writing, customer service and software development
• No complex setup: You can use generative AI right away – no need to define goals or connect tools to it

Despite these strengths, generative models still rely entirely on input to produce results.

Disadvantages of generative AI:

• Doesn’t track goals: Generative AI reacts to prompts – it doesn’t plan or follow through on tasks
• No process control: It can’t manage multi-step workflows or adjust based on progress
• Lacks memory between prompts: Each prompt is handled independently unless part of a live conversation
• Can’t self-correct: You have to review and evaluate the output yourself. There’s no built-in feedback loop

Agentic AI in detail

Agentic AI goes a step further than generative AI. Instead of just reacting to prompts, it works toward specific goals and plans how to achieve them, without needing constant user input.

Advantages of agentic AI:

• Works toward goals independently: Once a goal is defined, agentic systems break it down into subtasks and carry them out without further instructions
• Adapts decisions based on results: They evaluate what’s working, learn from feedback and adjust their strategy as needed
• Uses external tools and APIs: Agentic systems actively work with browsers, databases or system commands to complete tasks
• Keeps track of progress: They retain context from previous steps and use that information to guide subsequent actions
• Learns and adapts from mistakes: If something doesn’t go as planned, the system automatically revises its approach

But these extra capabilities also make agentic systems harder to build and manage.

Disadvantages of agentic AI:

• Takes more effort to set up: Planning, tool integration and memory handling all need to be coordinated
• Consumes more resources: Running agentic systems is often more resource-intensive than generative systems
• External access needs to be managed carefully: Agentic systems need clear rules for connecting to other systems
• Requires clearly defined goals: Agentic systems also need clear success criteria to work effectively
• Slower to build and deploy: Developing and testing agentic systems takes more time and effort than generative systems

When is each one a good fit?

Whether you use generative or agentic AI depends on what you’re trying to do. Each system has its strengths and works best in different situations, depending on how complex the task is, how much automation you need, and how much control the AI needs to have.

Use cases for generative AI

Generative AI is a great fit when you need to produce a lot of content quickly and consistently.

Common use cases include:

• Creating marketing content: Generative AI helps you write ad copy, social posts or product descriptions that match your brand voice and target audience.
• Editing and refining text: Writers use it to polish drafts, expand on ideas or shorten text to keep it focused.
• Writing and completing code: Developers use tools like GitHub Copilot to get suggestions for code, tests or documentation as they work.
• Improving customer support: AI-powered chatbots can handle common questions, categorise requests and suggest answers, helping customers get what they need faster.
• Sparking inspiration: Designers, writers and musicians use generative tools to come up with new ideas. AI can create everything from sketches to song snippets and writing samples.

These tasks benefit from how quickly generative AI can be put in place. It fits seamlessly into existing workflows without needing complex setup or major changes.

Use cases for agentic AI

Agentic AI is built for more complex tasks, like managing processes with many moving parts, handling dependencies or following through on long-term objectives. Unlike reactive systems, it evaluates information as it goes and adjusts its actions based on what it finds.

One powerful approach is Agentic RAG, which combines goal-driven planning with a retrieval component. This combination allows the system to pull in up-to-date information from external sources as needed. It then checks this information against its objectives and decides what to integrate into its next steps. As a result, the system not only retrieves information but also uses it strategically as part of an ongoing process.

Common use cases include:

• Automated research: Agents can check sources, organise information and decide what’s relevant for a given topic.
• Data processing and analysis: Agentic systems can run ETL tasks (extract, transform, load), verify results along the way and generate reports.
• Technical support: Agentic systems can diagnose problems, recommend solutions and escalate issues when needed.
• IT automation: Agentic systems can manage build pipelines, test components, handle deployments and roll back to stable versions if errors occur.
• Workflow management: In business settings, agentic systems can monitor tasks, allocate resources and update project plans as things change.
• Personalised learning tools: Agentic systems can track a learner’s progress, identify areas for improvement and recommend personalised learning paths.

These tasks call for systems that can handle uncertainty, learn from experience and adapt to changing conditions. Agentic systems make that possible, but also require high-quality data, clearly defined goals and close integration with other systems to work best.