Agentic AI vs Generative AI

Generative AI has dominated the conversation, writing emails, generating code, and even mimicking artistic styles with surprising flair. But another kind of AI is starting to make its mark, and it’s not here to wait for your prompt.

Agentic AI doesn’t just respond. It takes initiative. It sets goals, makes decisions, and completes tasks without constant input. It’s less “Tell me what to do” and more “Already on it.”

Understanding the difference between generative and agentic AI isn’t just a technical curiosity. It’s a strategic choice that affects how you build, secure, and scale the tools you depend on. As AI takes on more responsibility in your workflows, knowing what kind of system you’re working with and what risks come with it matters more than ever.

The AI you trust won’t just shape your output. It’ll shape how confidently and safely you move into the future.

What is Agentic AI?

Agentic AI flips the script on how we think about artificial intelligence. Instead of waiting for a prompt or reacting to inputs, these systems are built to act on their own. They can set goals, make decisions, and carry out tasks, often across multiple steps, without needing constant human direction.

That’s a big leap from traditional generative AI, which is great at producing text, code, or images when asked but doesn’t make independent choices. Generative AI can give you options — agentic AI picks one and runs with it.

Picture a virtual teammate who doesn’t just summarize a meeting but schedules follow-ups, updates your task board, and checks in with colleagues when something stalls. It’s proactive, persistent, and aware of context. That shift from generating content to taking initiative is what defines agentic AI.

As these systems become more capable, understanding their design, behavior, and risks becomes essential for engineers and anyone relying on AI to help get real work done.

Historical roots and evolution of agentic AI

Agentic AI has roots in early work on autonomous systems in robotics and cognitive science fields, exploring machines capable of perceiving, deciding, and acting independently. Initially, these ideas were mostly theoretical, but they laid the groundwork for today’s agentic models.

Generative AI took a different path, growing out of machine learning advances focused on pattern recognition and content creation. Models like GPT and Stable Diffusion made it possible to produce high-quality text, images, and code based on massive training datasets.

Agentic AI builds on that progress but adds something new: initiative. It doesn’t just respond. It chooses, plans, and executes. As these systems become more integrated into tools and environments, they move from creators to collaborators, shifting how we think about AI’s role in getting things done.

Key terminology and technical differences

Architectural considerations for generative AI vs agentic AI

The architectural differences between generative and agentic AI run deep:

Generative AI (e.g., LLMs):

• Designed primarily for single-turn, prompt-response interactions.

• Excels at predicting the next best output (e.g., a word, line of code, image pixel) based on its training data.

• Focuses solely on producing outputs, not on determining subsequent actions or maintaining context across multiple steps.

Agentic AI:

• Built for continuity and sustained operation.

• Combines an LLM with additional components such as:

  • Memory: To retain context and information across various steps.

  • Tools: To interact with and leverage external systems.

  • Planning Module: To set, monitor, and revise goals dynamically.

• Operates in continuous loops of observing, reasoning, and acting over time, rather than a one-and-done response.

This architecture enables agentic systems to function more like decision-makers than assistants. They can handle branching logic, adapt to new information, and course-correct when things change. That layered design is part brain, part toolkit, which gives them the flexibility to move from output to outcome.

Real-world applications of agentic AI vs generative AI

Generative and agentic AI aren’t just theoretical. They show up in real products, real workflows, and real decisions. But their roles look very different. Generative AI excels when the task is to create, complete, or summarize. Agentic AI, on the other hand, shines when the task requires action, adaptation, and follow-through. Here’s how each plays out in the wild:

Generative AI use cases

• Drafting marketing content and blog posts

• Autocompleting or generating code in IDEs

• Generating images and design concepts

• Powering chatbots and virtual assistants

Agentic AI use cases

• AI developer assistants that fix bugs, refactor code, and open pull requests

• Security agents that scan codebases, apply patches, and monitor for regressions

• Data cleanup bots that detect anomalies, correct records, and learn from past decisions

Bottom line: Generative AI supports workflows. Agentic AI drives them.

Governance and risk management of AI systems

As AI systems become more autonomous, the risks shift from what they say to what they do. With generative AI, the biggest concerns are typically hallucinations, copyright issues, and prompt injections. However, agentic AI introduces a new class of governance challenges because these systems persist, adapt, and act over time.

One primary concern is unpredictable autonomy. Agentic systems often make decisions without direct oversight, which makes it harder to trace their reasoning or audit their outcomes. To mitigate this, organizations are exploring concepts like the AI Bill of Materials (AI-BoM), a way to document and track the models, tools, and data an agent relies on, much like a software SBOM.

Another emerging risk is agent hijacking, where attackers manipulate an agent’s memory, tool access, or planning logic to push it toward harmful goals. Snyk’s recent analysis of agent hijacking threats highlights how subtle and dangerous these AI attacks can be, especially when agents operate without clear boundaries.

There’s also the issue of shadow AI: agents and models deployed without centralized oversight or proper security controls. These hidden systems can introduce compliance gaps and data exposure risks. Snyk outlines detecting and managing shadow AI before it undermines your security strategy.

Agentic AI can be transformative, but only if deployed with intention, transparency, and strong safeguards. Otherwise, autonomy becomes a liability, not a feature.

Key takeaways

• Agentic AI is goal-oriented, autonomous, and interactive.

• Generative AI produces content but does not act on it.

• Agentic systems raise new governance, security, and reliability issues.

• Enterprise leaders should prepare for both, especially with proper tooling.

• Tools like Snyk’s AI security platform are essential for protecting against GenAI and agent risks.

How Snyk helps

Agentic and generative AI aren’t just shaping the future. They’re actively reshaping how code gets written, tested, and deployed today. As teams embrace these systems, the need for trustworthy, auditable, and secure AI grows fast.

Whether you’re using generative tools to speed up development or exploring agentic systems that act on their own, now’s the time to build in safeguards. Snyk’s AI security platform helps you protect against risks introduced by AI-generated code, while DeepCode AI continuously improves quality and catch rate at the source. And for emerging threats like agent hijacking, Snyk provides visibility into how agents behave and when things go off track.

Schedule a demo to see how Snyk AI can improve your code security.