Agentic AI vs conversational AI: what’s the difference?

Can artificial intelligence only talk, or can it also think and act on its own?

That’s the key distinction between conversational AI and agentic AI. Conversational AI specializes in dialogue, understanding human language, and making interactions feel seamless. Agentic AI, however, takes things further by reasoning, making decisions, and carrying out multi-step tasks independently.

For businesses, this difference is more than technical. It shapes how customer queries are resolved, how workflows are automated, and how quickly outcomes are delivered. Conversational AI streamlines communication, while agentic AI drives execution, and together, they create a powerful model for efficiency and growth.

In this blog, we’ll break down agentic AI vs conversational AI: what they are, how they differ, real-world use cases, and why businesses can unlock the most value when the two work hand in hand.

What is agentic AI?

Agentic AI refers to artificial intelligence systems that can operate as independent agents, capable of setting goals, planning, and executing tasks with minimal human input. Unlike traditional AI models or conversational bots that respond to prompts, agentic AI is proactive. It can reason, make decisions, and take multi-step actions to achieve an outcome, rather than waiting for instructions at every step.

What makes agentic AI powerful is its autonomy. It doesn’t just process data or generate responses; it can figure out the best way to solve a problem and carry it through to completion. This makes it especially valuable in environments where speed, accuracy, and efficiency are critical, such as customer support, research, or operations.

What is conversational AI?

Conversational AI refers to technologies that enable machines to understand, process, and respond to human language in a natural, dialogue-based way. It powers chatbots, virtual assistants, and voice interfaces that can hold two-way conversations with end users.

At the core of conversational AI are Natural Language Processing (NLP) and Natural Language Understanding (NLU), which help systems interpret intent, context, and sentiment in human speech or text. This allows them to go beyond scripted responses and deliver more relevant, human-like interactions.

Unlike agentic AI, which takes independent actions, conversational AI is primarily reactive. It responds to prompts and questions but typically relies on either humans or other systems to carry out complex tasks.

Head-to-head comparison between agentic AI and conversational AI

While both agentic AI and conversational AI aim to improve how businesses interact with their customers, they differ fundamentally in scope, capabilities, and outcomes. Here’s a side-by-side comparison to make the differences clear.

In essence, conversational AI is a type of AI agent that focuses on understanding and responding to queries, while agentic AI takes the next step by autonomously executing tasks and driving outcomes. The distinction between agentic AI vs AI agents lies in the level of autonomy: while conversational AI interacts, agentic AI takes action to achieve results.

Businesses often use both together, with conversational AI to engage and agentic AI to act, creating a complete, end-to-end customer experience.

Key differences between agentic AI and conversational AI

While the comparison table offers a quick overview, it’s worth diving deeper into how agentic AI and conversational AI differ in practice. These differences highlight not just what each technology does, but where they create the most value for businesses today.

1. Core capabilities

Conversational AI excels at interpreting human language and engaging in natural, dialogue-based interactions. Its primary strength lies in handling repetitive queries, guiding users through simple processes, and ensuring fast, consistent communication across multiple channels. For instance, it can provide account information, answer FAQs, or guide a customer through troubleshooting steps, all without involving a human agent.

Agentic AI is built around action and execution. Its core capability is the ability to reason, plan, and carry out multi-step tasks independently. Instead of stopping at conversations, it can analyze data, decide on the best solution, and complete the task from start to finish, like approving a refund, scheduling a delivery, or generating a detailed business report.

2. Interaction style

Conversational AI is reactive in nature. It waits for users to ask questions or raise concerns and responds accordingly, creating the experience of a human-like conversation. Its focus is on providing clarity, empathy, and assistance within the boundaries of dialogue. But it relies on other systems or people to carry out tasks beyond the chat window.

Agentic AI takes a proactive role. It doesn’t just answer questions but anticipates needs and initiates actions on its own. For example, if it detects that a delivery is delayed, it can automatically notify the customer, process compensation, or reschedule the shipment without waiting for the customer to complain. This makes interactions more solution-oriented and less dependent on back-and-forth dialogue.

3. Decision-making and autonomy

Conversational AI has limited decision-making capabilities. It follows predefined flows, Natural Language Processing models, and intent recognition systems to provide the most accurate response. While it may personalize answers based on customer data, it does not independently decide on the next course of action.

Agentic AI operates with a higher level of autonomy. It can set goals, evaluate multiple options, and decide which actions are most appropriate in a given context. This autonomy enables it to act like a human agent and handle complex situations, like prioritizing customer issues, resolving operational bottlenecks, or adjusting marketing campaigns.

4. Complexity and scalability

Conversational AI is relatively easier to scale. Once trained and integrated with a knowledge base, it can handle thousands of customer interactions simultaneously. This makes it ideal for organizations that need to reduce response times and support costs without heavily reengineering their existing systems.

Agentic AI, however, is more complex to deploy and scale. It requires deeper integration with business processes, workflows, and data systems to function effectively. But the payoff is significant. Each AI agent that’s deployed doesn’t just answer queries; it takes work off human teams by completing tasks, making scaling agentic AI directly tied to scaling business output.

5. Real-world applications

Conversational AI is commonly seen in customer support chatbots, virtual banking assistants, healthcare information bots, and retail product recommenders. These applications are focused on answering questions quickly, providing guidance, and reducing the burden on human agents for routine interactions.

Agentic AI is used in scenarios where action is as important as communication. Examples include automatically processing refunds in eCommerce, conducting market research by pulling and analyzing data, and running end-to-end onboarding workflows. These applications highlight its role as a problem-solver rather than just a conversational partner.

Benefits of using conversational AI and agentic AI together

When conversational AI and agentic AI are combined, they create a more powerful support ecosystem. Such ecosystems not only communicate with customers but also take meaningful action to resolve their needs, improving the overall customer experience. Here are the key benefits of bringing the two together:

• Bridge conversations with actions: Conversational AI understands intent, and agentic AI executes tasks, ensuring queries lead to outcomes, not just answers

• Faster resolutions: Customers get issues solved in one flow, with fewer delays and no unnecessary hand-offs

• Lighter workload for agents: Repetitive tickets are handled automatically, letting agents focus on high-value, complex cases

• Better customer experience: Instant replies combined with end-to-end problem-solving create smoother, more satisfying interactions

• Scalable support: Businesses can manage growing interaction volumes without expanding agent teams

• Consistent service quality: Automated handling reduces errors and ensures every customer receives the same standard of support

• Cost efficiency: Lower operational costs by reducing manual intervention while maintaining service coverage

Real-world use cases of agentic AI and conversational AI

Both conversational AI and agentic AI are already transforming how businesses operate. Here are a few use cases of agentic and conversational AI to give you an idea of how these artificial intelligence approaches work in real-life scenarios:

Use cases of agentic AI

1. Autonomous customer service

Agentic AI auto-resolves common customer issues end-to-end without human intervention. For instance, if a customer requests an update to their billing details or needs to reset login credentials, an AI customer support agent can authenticate the request, make the necessary changes, confirm them with the customer, and log the interaction, all within minutes.

One such platform is Freshdesk, an agentic AI customer service solution that empowers businesses with smart AI agents. Freshdesk’s Freddy AI automatically handles customer queries, improving response times by 83% and agent productivity by 60%. This doesn’t just reduce ticket backlogs but also ensures customers get instant resolutions without waiting in queues.

2. Automating refunds and returns in eCommerce

Retailers use agentic AI to manage returns at scale during peak shopping seasons. Instead of passing refund requests to multiple teams, the AI validates the order, checks policy eligibility, processes the return, generates shipping labels, triggers a replacement, and sends proactive updates to the customer. This helps cut operational costs while improving customer trust.

3. AI-driven marketing campaigns

Global brands leverage agentic AI to launch campaigns in real-time based on customer actions. For example, when a customer abandons a cart, the AI automatically segments them into a retargeting group, pushes a personalized discount, and schedules a follow-up reminder. Marketing teams gain agility while maintaining precision in execution.

4. Research and data analysis at scale

Financial institutions and research firms use agentic AI to comb through unstructured data like reports, journals, and market updates. Instead of manual analysis, the AI autonomously extracts patterns, highlights risks, and suggests opportunities. A hedge fund, for instance, can receive daily summaries of market shifts, enabling faster and better-informed investment decisions.

Use cases of conversational AI

1. Customer support chatbots for L0 queries

Businesses with high support call volumes use customer service chatbots like the one offered by Freshdesk to deflect repetitive queries like order tracking, payment confirmations, or password resets. Airlines, for example, allow customers to check flight status or baggage information instantly through chatbots, which reduces pressure on contact centers.

2. Voice assistants for everyday tasks

Smart device ecosystems are powered by conversational AI that simplifies user interactions. From managing smart home lighting to setting up reminders, voice assistants like Alexa or Google Assistant have become productivity tools in both households and workplaces.

3. Guided self-service in banking, healthcare, and retail

Banks are enabling customers to check balances, transfer funds, or block cards through conversational bots, while hospitals use them to help patients book appointments or access lab results. Retailers deploy these bots as virtual shopping assistants that guide users toward the right products or offers in real time.

4. Lead generation and qualification in sales

SaaS companies and B2B businesses rely on conversational AI to engage visitors on their websites. Bots greet prospects, gather qualifying details such as industry and team size, and route them directly to the right sales representative or provide a demo link. This reduces lead drop-offs and speeds up conversions.

Level up your customer support operations with Freshdesk’s smart agentic AI

Agentic AI is becoming the backbone of modern customer service, enabling businesses to move beyond simple query handling toward full end-to-end resolutions. Instead of stopping at gathering customer queries, agentic AI can validate requests, take the right actions, and close the loop without a support agent ever stepping in. This unlocks faster response times, less operational friction, and greater customer satisfaction.

Freshdesk brings this future to life with Freddy AI, a built-in intelligent solution trusted by over 73,000 brands worldwide. Freddy AI doesn’t just respond to customer queries; it acts on them. From updating account details and processing returns to routing tickets and triggering automated workflows across integrated tools, Freddy AI ensures tasks are completed quickly and accurately.

Freddy AI delivers an average resolution time of under two minutes, helping businesses provide support that is fast, reliable, and always available. By combining conversational efficiency with agentic execution, Freshdesk’s Freddy AI enables businesses to scale support operations without scaling headcount, reduce agent workload, and deliver seamless customer experiences that leave a lasting impression.

Want to know more about how Freddy AI can help streamline your customer support operations? Book a personalized demo today.