Agentic AI vs. generative AI: The key differences

AI has evolved rapidly over the past few years—from tools that generate text and images to systems that can plan and act. As this landscape matures, two major branches of AI are starting to emerge more distinctly: generative AI and agentic AI.

If you’re leading a contact center, sales team, or any high-velocity communication function, understanding the difference between these technologies is vital.

Let’s unpack what makes them different, where they intersect, and how each one is used across modern workflows.

What is the difference between generative AI and agentic AI?

Generative AI and agentic AI serve different but complementary roles in modern AI systems. Generative AI focuses on creating content, such as text, images, or code, based on user prompts, while agentic AI focuses on taking action by planning, deciding, and executing multi-step tasks toward a goal. Generative AI responds when asked, whereas agentic AI determines what needs to be done next and does it. This distinction is especially important in business and CX use cases, where efficiency depends not just on generating information, but on acting on it autonomously.

Agentic AI vs generative AI: key differences

Generative AI is designed to create content. It uses machine learning models (often trained on massive datasets) to produce new outputs based on input prompts. This includes generating human-like text, realistic images, voice responses, or even code. Tools like ChatGPT, DALL·E, and Midjourney are well-known examples. In business, generative AI often shows up in features like email drafting, meeting summaries, call transcripts, or chatbot scripting.

Agentic AI, on the other hand, refers to AI systems that can plan, initiate, and complete multi-step tasks autonomously. Instead of just generating an output when asked, agentic AI acts more like a collaborator—analyzing goals, breaking them into steps, making decisions based on feedback loops, and adapting to changing conditions without constant human input.

In other words:

• Generative AI creates content (like text, images, or videos).

• Agentic AI decides what to do, and does it (sometimes by using generative AI along the way).

How agentic AI and generative AI work together

While generative AI and agentic AI are distinct, their real power emerges when they’re used together in the same workflow. In customer experience (CX) environments, generative AI provides the intelligence, while agentic AI provides the execution. Generative models interpret conversations, draft responses, and extract insights, while agentic systems decide what actions to take, such as escalating an issue, updating a ticket, or triggering follow-ups, without waiting for human direction. Together, they enable faster resolution times, reduced agent workload, and more consistent customer outcomes.

Example: Agentic AI + generative AI in a CX workflow

Step 1: Generative AI interprets and creates

• Analyzes a customer conversation

• Summarizes intent and sentiment

• Drafts a recommended response or resolution

Step 2: Agentic AI plans and acts

• Determines next best action based on goals and policies

• Assigns or prioritizes the ticket

• Sends follow-ups, updates systems, or escalates issues automatically

Step 3: Continuous improvement

• Agentic AI monitors outcomes

• Uses feedback loops to refine future decisions

• Calls generative AI again as new content is needed

In this model, generative AI answers the “what,” while agentic AI handles the “what next.” This collaboration is increasingly central to how autonomous, scalable AI-driven CX platforms operate, and explains why discussions around how does agentic AI work often include generative AI as a foundational component rather than a competing approach.

Use cases for generative and agentic AI

Both types of AI are powerful, but they shine in different areas. Let’s look at some examples of how they’re used in contact centers, sales outreach, internal communications, and industries adopting AI at scale:

Contact centers

Generative AI – Can automatically summarize support calls, suggest responses in real time, generate help center articles, and transcribe voice interactions

Example: Dialpad Support uses generative AI to create real-time transcripts and call summaries to help agents stay focused.

Agentic AI – Can manage full ticket workflows, including monitoring support queues, logging details in the CRM, and closing the case without human intervention

Example: An agentic AI could detect a pattern of negative sentiment across multiple customer chats, auto-prioritize those tickets, initiate a resolution process, and notify a supervisor before it becomes a broader churn issue.

Fun fact: Dialpad is currently developing an agentic AI platform to allow customers to create practical, fully autonomous, applications such as:

• Order tracking – Automatically authenticating customers, accessing order systems, and providing real-time shipping updates with delivery estimates and tracking options.

• Appointment management – Rescheduling appointments, showing available time slots, and updating scheduling systems automatically.

• Recruitment screening – Conducting initial candidate interviews, collecting qualifications, storing information in HR systems, and scheduling applicants for interviews with the recruitment team.

Sales outreach

Generative AI – Can write cold email drafts, personalize outreach messages, and create pitch decks

Example: A sales outreach tool that uses generative AI to help reps draft follow-up emails and generate call recaps post-demo.

Agentic AI – Can automate and adjust outreach sequences based on lead behavior

Example: An agentic system could manage a rep’s follow-up strategy across an entire pipeline, initiating reminders, re-prioritizing hot leads, and scheduling demos based on AI analysis of intent signals.

Internal communications

Generative AI – Can summarize meetings, draft internal newsletters, and create knowledge articles from documentation

Example: Automatically creating an internal summary after a cross-functional team call.

Agentic AI – Can coordinate follow-ups, schedule meetings based on availability and context, and nudge stakeholders to complete assigned tasks

Example: After a team meeting, an agentic AI generates action items, assigns them to team members, and follows up on deadlines.

Healthcare operations

Generative AI – Can write patient summaries, transcribe doctor-patient interactions, draft clinical notes, and generate educational materials tailored to patient diagnoses

Example: A clinician uses generative AI to create post-visit instructions for a patient based on the notes from a telehealth session.

Agentic AI – Can manage patient intake, automate scheduling and reminders, and coordinate multi-step care plans across departments

Example: An agentic system could automatically follow up with patients for lab work, update the provider’s schedule, and notify staff when test results require urgent attention.

HR and Recruiting

Generative AI – Can create job descriptions, draft offer letters, write candidate communications, and generate interview questions based on role requirements

Example: A recruiter uses generative AI to instantly draft a personalized follow-up email to a candidate after their first-round interview, pulling in role details and notes from the hiring manager.

Agentic AI – Can manage end-to-end recruiting workflows including screening resumes, scheduling interviews, tracking progress in the ATS, and following up with hiring teams and candidates

Example: An agentic AI reviews inbound applications, shortlists qualified candidates based on job fit, schedules them for initial interviews, and nudges hiring managers to complete feedback forms — all without manual coordination.

Financial services

Generative AI – Can create personalized investment summaries, customer reports, and educational content for clients

Example: A financial advisor uses generative AI to generate a tailored retirement outlook summary based on portfolio data.

Agentic AI – Can handle fraud detection workflows, automate compliance reporting, and onboard a new client (e.g., collecting documents and verification).

Example: An agentic AI monitors transactions for suspicious activity, triggers verification requests, and escalates issues to the fraud team only when human review is needed.

eCommerce and retail

Generative AI – Can write product descriptions, auto-generate customer responses for product questions, and personalize abandoned cart emails

Example: A marketing team uses generative AI to create unique product copy at scale for a catalog of thousands of SKUs.

Agentic AI – Can track customer behavior, manage automated return/refund flows, and optimize fulfillment logistics based on order patterns and inventory

Example: After identifying repeated return requests for a product, an agentic AI initiates a product flag for review and begins proactive outreach to impacted customers.

Logistics and supply chain

Generative AI – Can draft shipment updates, generate route documentation, or create reports for internal logistics planning

Example: A warehouse uses generative AI to produce shift briefings and incident logs based on system data.

Agentic AI – Can orchestrate end-to-end delivery planning including inventory balancing, re-routing based on traffic/weather, and supplier coordination

Example: An agentic AI reassigns shipments in real time due to supply chain delays and automatically notifies vendors and customers of new ETAs.

AI agents vs. agentic AI: What’s the difference?

AI agents and agentic AI are closely related concepts, but they are not interchangeable. An AI agent is a specific entity or component designed to perform a defined task, such as answering a question or completing a single action. Agentic AI, by contrast, is a system-level approach that coordinates one or more AI agents to autonomously plan, decide, and execute multi-step workflows. In other words, AI agents are the building blocks, while agentic AI is the architecture that enables agents to work together toward broader goals. Understanding this agentic AI vs agent AI difference is key when evaluating autonomous AI platforms.

Risks and limitations: Where each can fall short

No AI system is perfect. Understanding where each approach has blind spots is essential for making smart implementation decisions.

Generative AI risks

Generative AI can produce high-quality outputs, but it’s not always reliable. Without context or oversight into training datasets, it may “hallucinate” facts, offer outdated information, or generate content that sounds confident but is incorrect. It also lacks memory by default, which means it can’t learn from previous interactions unless it’s paired with external systems. For customer-facing teams, this means that any output from a generative AI model (whether it’s a support summary or a sales email) should still be double-checked.

Agentic AI risks

Agentic AI adds autonomy and decision-making, but that also introduces more complexity. These systems are designed to act on their own, monitor workflows, trigger follow-ups, reprioritize tasks, and orchestrate outcomes. Without proper constraints, poorly designed agentic systems could misinterpret data or take actions that are out of sync with business policies.

It’s important to note that these risks are not inherent to agentic AI—they often stem from poor implementation or tools. Look for secure, enterprise-grade AI platforms that support clear task boundaries, audit trails, and integration governance to mitigate and minimize risk.

At Dialpad, agentic AI features are being developed with robust data privacy, role-based access controls, and transparent decision logic. That means organizations can deploy AI agents confidently—knowing they’ll operate within approved workflows, respect security policies, and escalate when human judgment is needed.

How agentic AI will shape the future of work

Beyond taking on basic tasks, agentic AI is a systems-level thinker that can orchestrate multiple tools, manage workflows, and learn from results. It’s especially powerful in high-stakes, high-volume environments like contact centers and outbound sales teams, where timing, prioritization, and follow-through are critical. In fact, Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention, leading to a 30% reduction in operational costs.

That doesn’t mean generative AI is going away. In fact, many agentic AI systems rely on generative models to carry out parts of their work. But the shift from reactive content generation to autonomous action marks a significant step forward in how AI will support business operations moving forward.

Dialpad agentic AI: Autonomous agent assistance

Agentic AI is Dialpad’s next-generation innovation. It is an in-house-built, autonomous agent assistance system that transforms how organizations work. It’s designed for real-time engagement and works seamlessly across all customer channels.

Key agentic AI features:

• Omnichannel coverage:
  Deliver a unified experience across voice, video, and digital messaging channels, ensuring customers get consistent, intelligent responses—no matter how they reach out.

• Natural conversation:
  Provide a natural, low-latency voice experience that feels truly human. It's purpose-built for high-quality real-time interactions, not retrofitted from chat.

• Autonomous assistance:
  Agentic AI supports customers and agents in real time with intelligent actions like handling routine queries, scheduling appointments, and checking order status—without needing human intervention.

• Seamless escalation to human agents:
  When escalation is required, Dialpad’s AI Agent instantly hands off to human agents with full conversational history and context—eliminating redundancy and improving resolution times.

• Scalability with insights:
  Easily deploy multiple bots for multiple channels, without sacrificing on insights. Drive better business outcomes from analytics across any interaction: voice, chat, and more. 

As AI continues to evolve from generating content to taking action, contact centers are at the forefront of where this shift matters most. Today’s CX teams need more than insights—they need systems that can reduce manual effort, guide agents in real time, and lay the groundwork for smarter automation. Dialpad’s contact center solution is built with this future in mind, combining real-time AI assistance and workflow intelligence that helps teams work faster and more effectively now, while setting the stage for more agentic capabilities as the technology matures.