Agentic AI vs. Generative AI: 5 Key Differences

Defining Agentic AI and Generative AI

Agentic AI and Generative AI, while both part of the broader AI landscape, have distinct focuses. Generative AI excels at creating new content (text, images, code, etc.) based on patterns learned from data. It’s like a creative assistant that responds to prompts. Agentic AI focuses on autonomous action and goal achievement. It perceives, reasons, acts, and learns to complete tasks with minimal human oversight. Think of it as a proactive problem-solver that can automate workflows.

Generative AI:

• Focus: Creating new content. Mechanism: Learns from data and generates outputs based on learned patterns. 
• Examples: Large language models like GPT-4, diffusion models for images. 
• Core function: Responds to prompts, generating text, images, code, etc. 
• Analogy: A creative assistant or a sophisticated chatbot.

Agentic AI:

• Focus: Autonomous action and goal achievement. Mechanism: Perceives its environment, makes decisions, and takes actions to achieve goals, often using tools. 
• Examples: AI systems that automate complex workflows, like OpenAI’s Operator and Google’s Project Mariner. 
• Core function: Executes tasks, makes decisions, and adapts to changing circumstances. 
• Analogy: A proactive colleague or a digital assistant.

Key differences:

• Workflow automation: Agentic AI is well-suited for automating workflows and simplifying processes, while generative AI is more focused on content creation..
• Autonomy: Agentic AI is designed to operate with more autonomy than generative AI, which typically relies on prompts. 
• Goal orientation: Agentic AI is inherently goal-oriented, whereas generative AI is content-creation oriented. 
• Decision making: Agentic AI makes decisions and takes actions, while generative AI primarily focuses on content generation. 

Generative AI Is the Foundation for Agentic AI

Modern agentic AI systems are built on top of large language models (LLMs). The reasoning, planning, and decision-making abilities of these systems come from the same generative foundations that produce text, code, or images. LLMs provide the ability to parse natural language, infer intent, and generate structured actions that agents can use to progress toward goals.

For example, when an agent needs to plan a workflow, it uses an LLM to break a broad objective into subtasks. The model generates the reasoning steps and action sequences, which the agent can then execute by calling APIs, retrieving data, or interacting with tools. The LLM acts as the “cognitive engine,” while the agent framework handles memory, state tracking, and action execution.

This integration allows agentic systems to operate flexibly in open-ended environments. Without generative models, agents would be limited to rule-based logic or fixed workflows. By embedding LLMs, agentic AI gains the ability to adapt, improvise, and recover from unexpected conditions while still working toward long-term goals.

In practice, this means that advances in generative AI directly expand the capabilities of agentic AI. Improvements in reasoning, summarization, and contextual understanding in LLMs make agents more reliable in managing multi-step processes, adapting to new inputs, and collaborating with humans.

Agentic AI vs. Generative AI: Key Differences 

1. Focus and Goals

Generative AI is task-focused and reactive. Its primary goal is to generate content based on direct prompts from users. Each task is self-contained — for example, writing a paragraph, summarizing a document, or creating an image. It doesn’t maintain continuity between tasks or work toward any long-term objective. Once the content is produced, the process ends unless a new prompt is given.
Agentic AI is goal-oriented and proactive. Rather than waiting for individual prompts, it starts with a defined objective and works through multiple steps to accomplish that goal. It continuously assesses progress and determines what needs to happen next. For instance, if the goal is to plan a vacation, an agentic system might break that down into subtasks like finding flights, checking weather, booking hotels, and coordinating with other travelers — executing each subtask autonomously.

2. Core Function

The core function of generative AI is content creation. It generates output such as text, images, or code by identifying patterns in large datasets it was trained on. It’s best used for single-step tasks, where the user provides specific inputs and expects immediate, relevant output. Examples include creating blog drafts, summarizing reports, writing email replies, or generating visual assets.

The core function of agentic AI is to manage and execute multistep tasks. It performs processes that require chaining actions together to reach a desired outcome. It reasons, plans, and acts across several layers of logic. For example, in research, an agentic AI could find sources, extract relevant data, draft a report, and adjust its strategy based on what it finds.

3. Autonomy

Generative AI has low autonomy. It is passive and depends entirely on the user to drive interactions. Every action it takes is triggered by a prompt, and it doesn’t retain context between different tasks unless explicitly designed to do so within a session. It doesn’t initiate tasks or modify its behavior unless retrained.

Agentic AI exhibits high autonomy. Once given an overarching goal, it can independently plan and carry out actions, using feedback from its environment or outputs to inform the next steps. It uses its own decision-making process to stay on track and complete objectives, only asking for human intervention when encountering ambiguity or specialized knowledge it cannot resolve on its own.

4. Workflow Automation

Generative AI plays a supporting role in workflow automation. It helps with individual steps like writing, editing, or translating, but it cannot manage an entire process from start to finish. It must be guided by a person at each stage, which limits its ability to drive full workflows without human supervision.

Agentic AI enables full workflow automation. It’s built to carry out sequences of actions that require coordination, decision-making, and adjustment. For example, in project planning, it can manage scheduling, send communications, update timelines, and handle unexpected changes. This makes it useful for industries like compliance, research, and software development, where tasks span multiple systems and steps.

5. Decision Making

Generative AI makes decisions at a basic level. It selects the next word, image component, or code segment based on statistical likelihood derived from training data. It does not evaluate alternatives or reason through consequences — its choices are driven by pattern recognition, not goal alignment or strategic thinking.

Agentic AI performs complex decision-making. It weighs multiple options, considers expected outcomes, and chooses the best course of action based on current conditions and its overall objective. It can use tools, interact with APIs, and learn from its actions through feedback loops. This allows it to adapt in real time, improve future behavior, and handle situations that require reasoning and judgment.

Related content: Read our guide to agentic AI tools (coming soon)

Tips from the expert

Steve Moore is Vice President and Chief Security Strategist at Exabeam, helping drive solutions for threat detection and advising customers on security programs and breach response. He is the host of the “The New CISO Podcast,” a Forbes Tech Council member, and Co-founder of TEN18 at Exabeam.

In my experience, here are tips that can help you better leverage agentic AI and generative AI in compliance and security contexts:

Design feedback loops for self-correction: Build agentic AI pipelines that can cross-check their own outputs against policy or compliance rules before finalizing actions.

Use controlled autonomy thresholds: Define clear decision boundaries where the agent must escalate to a human before proceeding—especially for regulatory, financial, or security-impacting decisions.

Embed provenance tracking in every step: Make the AI log not just its outputs, but also its reasoning paths, tool calls, and intermediate states for forensic audits.

Harden tool/API integrations: Apply strict authentication, input validation, and least privilege when giving agentic AI access to tools or external APIs to prevent escalation-of-function risks.

Combine model diversity for error resilience: For high-stakes outputs, have multiple AI models (agentic or generative) produce results independently, then compare them for discrepancies before acting.

Use Cases for Generative AI

Generative AI is widely used in content-heavy workflows where speed, consistency, and scalability are key. One of its most common applications is in content creation. Marketing teams and agencies use generative tools to produce large volumes of keyword-optimized blog posts, landing pages, and web copy. This allows them to increase publishing frequency and improve search engine rankings with minimal manual effort.

In marketing and sales, generative AI supports human teams by automating routine communication tasks. Virtual assistants and chatbots powered by generative models can handle lead outreach, follow-ups, and other interactions. This reduces time spent on administrative work, allowing sales professionals to focus on closing deals and building relationships.

Generative AI also improves customer support automation. It powers systems that respond to common inquiries in real time, such as order tracking or refund requests. For eCommerce businesses, this means fewer support tickets for humans to handle, faster response times for customers, and improved service consistency across platforms.

Another use is in log and network traffic analysis. Generative models can create synthetic but realistic datasets that mimic attack behavior. This allows security systems to be trained on rare or emerging threats without relying only on limited real-world samples. By improving the variety of training data, detection systems become more resilient against new attack techniques.

Generative AI also supports incident response. It can help reconstruct attack timelines by generating possible scenarios from incomplete or fragmented data. This accelerates investigations and reduces the time attackers stay undetected in systems. For large enterprises, this means faster recovery and less damage from breaches.Vulnerability Management

Use Cases for Agentic AI

Agentic AI is suited to tasks that require autonomy, adaptation, and sustained action over time. In cybersecurity, Agentic AI can operate as an active defense layer in cybersecurity by continuously monitoring systems and responding to threats in real time. Unlike static detection tools, it can adapt its strategies as attackers change tactics. For example, if suspicious behavior is detected on a network, an agentic system can isolate affected devices, block malicious traffic, and escalate alerts to security teams without waiting for human approval.

In customer service, it goes beyond traditional chatbots by understanding user intent and emotional tone, allowing it to take proactive steps to resolve issues. It can automate backend tasks like retrieving or formatting data, enabling smoother interactions and reducing human workload.

In healthcare, agentic systems can be embedded in smart devices to monitor patient conditions and environmental factors. A notable example is Propeller Health’s smart inhalers, which collect real-time data on medication use and air quality. These devices use agentic AI to alert providers when intervention is needed, enabling more responsive and personalized care.

Automated workflow management is another key application. Agentic AI can oversee end-to-end business processes without human oversight. For instance, in logistics, it can dynamically reroute deliveries based on live traffic data and shipment urgency. This not only reduces delays but also improves operational efficiency.

In financial risk management, agentic AI can continuously analyze market trends and adjust strategies in response to economic changes. It can monitor credit risk, optimize investment portfolios, and act on new data in real time. A fintech company might use it to rebalance assets as market conditions shift, aiming to protect client investments while maximizing returns.

Agentic AI vs. Generative AI in Cybersecurity 

Generative AI contributes to security workflows primarily by enhancing analysis and communication tasks. It can summarize lengthy incident reports, generate threat intelligence briefings, and assist with writing security policies or documentation. Generative models are also useful in phishing detection, where they help generate potential attack variants for training and testing defense systems.

However, generative AI lacks real-time responsiveness and decision-making capabilities. It cannot monitor systems, trigger alerts, or take actions on its own—limiting its role to support functions within a human-led security process.

Agentic AI, in contrast, can operate as a real-time decision-making layer within security environments. It monitors activity across networks, endpoints, or cloud systems and takes immediate action in response to detected threats. For instance, it can isolate compromised systems, revoke credentials, or initiate forensic logging automatically. This ability to perceive, decide, and act enables agentic AI to function as an autonomous threat response agent.

Agentic AI also supports continuous compliance by scanning for configuration drift, enforcing access controls, and remediating policy violations. When integrated with security information and event management (SIEM) systems, it can triage alerts, correlate signals across sources, and escalate only high-priority incidents—reducing alert fatigue and response time.

The key difference lies in execution: generative AI helps humans understand and communicate about security, while agentic AI can directly manage security operations with minimal intervention.

Agentic AI in the SOC with Exabeam

Exabeam Nova is a coordinated system of AI agents embedded in the New-Scale Security Operations Platform. Each agent supports a specific stage of the detection, investigation, and response workflow, helping analysts, engineers, and security leaders work faster and with greater consistency. Unlike standalone assistants, Exabeam Nova is fully integrated, so there are no extra tools, costs, or disjointed workflows.

How Exabeam Nova Applies Agentic AI

• Perceive: Builds behavioral baselines across users, entities, and AI agents, detecting subtle deviations that may indicate compromise.
• Reason: Applies adaptive risk scoring and correlates events into threat timelines that highlight what matters most.
• Act: Automates evidence collection, triage, and case creation, reducing mean time to detect and respond.
• Advise: Provides daily posture insights through the Advisor Agent, mapping detections to frameworks like MITRE ATT&CK and recommending improvements over time.

Key Benefits for Security Teams

• Faster investigations with auto-built timelines and case summaries.
• Higher analyst productivity by offloading repetitive tasks such as log parsing and report generation.
• Reduced burnout through guided workflows that help analysts at any skill level operate effectively.
• Stronger program visibility for leaders through quantifiable outcomes and posture reporting.

Example SOC Outcomes

• Up to 80% faster investigation time with automated timelines.
• 50% faster incident response by reducing manual steps.
• 60% reduction in irrelevant alerts with risk-based prioritization.
• Improved compliance through centralized logging and reporting.

Bottom line: Exabeam Nova operationalizes agentic AI for the SOC, pairing behavioral analytics with safe automation so teams can detect, explain, and respond with speed and confidence.