AI Agents vs AI Assistant: What is the Difference?

Intro

Contact centers across the globe are adopting AI to improve customer service, streamline operations, and reduce costs. AI within organizations is now commonplace, with 78% of all businesses saying they use AI in at least one business function.1

With the growth of AI on a global scale, there have never been more products, tools, and technologies offered to your enterprise. Though many of these tools share similar functionality that relies on Generative AI, Conversational AI with NLU, or a combination of both, they are often described in vastly different ways depending on the provider. 

This is evident in comparing an AI Agent and an AI Assistant. Though both tools rely on similar technology to operate, their function and scope make them distinct. We’ll explore this in detail down below, but the headline differences are:

1. AI Assistants, made popular by the likes of Alexa and Siri, are reactive and rely on user prompts to execute a task.
2. AI Agents are often associated with Agentic AI. They are proactive and capable of autonomous action. They can identify tasks, plot goals, and follow them – all without human intervention. 

In this article, we’ll break down the difference between an AI Agent and an AI Assistant to make sure you make the right choice when adopting AI in your contact center…

(Please note: there are often overlaps between these two terms, and many AI providers use them interchangeably. Here at Cognigy, we use AI Agent as our preferred term for both process-driven and more autonomous AI Agents.)

What Are AI Assistants?

The term AI Assistant is commonly associated with consumer technology, such as Amazon’s Alexa, Google’s Gemini, and Apple’s Siri. All of these assistants function in a similar manner: taking a user’s prompt, identifying the context and intent behind it, and then carrying out the action. 

Early digital assistants were more like chatbots and relied on keyword recognition and pre-defined conversational flows to serve users, which often led to frustrating experiences whenever a user strayed from the expected parameters of a conversation. (See chatbots vs AI Agents to learn more). 

AI has supercharged the capabilities of digital assistants. With access to Large Language Models (LLMs), AI Assistants can quickly understand queries and offer relevant, contextual responses. They rely on human interaction to function and aim to assist and support with all manner of queries, tasks, and problems. 

Key attributes of AI Assistants 

1. Reactive: AI Assistants rely on user input. They must wait for users to interact before they can provide relevant information, answers, or suggest actions. They can only complete an action when a user approves it. 
2. Supportive: Most AI Assistants are trained to support human activity rather than replace it. They function as copilots that offer recommendations, tailor insights and answer questions. 
3. Specific: AI Assistants are trained to complete specific tasks and follow pre-defined processes. 
4. Contextual: through the use of Conversational AI, assistants can understand the context and nuance of a user’s prompt and use that to tailor a personalized, specific response. 

Limitations

1. Prompt-reliant: AI Assistants need user prompts to operate effectively. They can’t take any action outside of the scope of the prompt. 
2. Narrow scope: AI Assistants are trained to facilitate specific functions for their users. They are focused on completing the tasks they have been trained to do and can’t innovate or problem-solve outside of them. 

What Are AI Agents?

In customer service, the term AI Agent has been in use for a number of years and often describes AI systems that were similar to what we’ve just described for AI Assistants. However, the rise of Agentic AI has reshaped the concept of AI Agent. 

With Agentic AI, AI Agents become autonomous systems that are capable of operating without human supervision. They are connected with your backend technologies, such as your CRM system, to enable them to gather information and take action. They don’t rely on human instruction; instead, they use machine learning and data analysis to assess input, reason their way through problems, plot their own workflow, and take action. Throughout every interaction, Generative AI Agents continue to produce relevant, personalized content in response to user queries. 

Where other types of AI are task-driven, AI Agents are goal-driven. They identify a user’s needs, plan tasks, and then carry them out without the need for additional prompts. This ability to pursue proactive action makes AI Agents the superior choice in applications that tackle complex, multi-step problems such as customer service.

Key Features of AI Agents

1. Proactive: AI Agents don’t rely on continual user prompting to be effective. They are fully autonomous and carry out tasks without any additional input. 
2. Independent: Where an assistant makes recommendations, an AI Agent evaluates potential solutions and then independently chooses the best one. 
3. Continual evolution: with short and long-term memory, AI Agents can call on past interactions to improve future processes. They don’t just rely on initial training and will continue to evolve as they act. 
4. Multi-step task completion: AI Agents don’t consider tasks to be isolated ‘one-off’ things. They break down a user’s problems into manageable steps, completing multiple ‘tasks’ on their way to solving it. 

Limitations

1. Lacks structure: though autonomous action makes AI Agents much more efficient, they can act in unexpected ways that may make them unsuitable for processes such as insurance claims that require adherence to strict parameters. 

What Is the Difference Between AI Agents and AI Assistants?

The difference between AI Virtual Agents and AI Assistants comes down to proactivity vs reactivity. AI Assistants are user-focused and react to a prompt by carrying out a specific task. AI Agents are goal-oriented and use an initial conversation to determine a user’s goal, break that down into tasks, and then dynamically reason their way through until it has best solved the challenge.

If you’re still unsure, here’s a table summarizing the main differences… 

What Are the Use Cases of AI Agents?

One of the best ways to visualize how a technology can be used is to look at use cases. Here are some of the best AI Agent use cases and examples that show how AI can be used to support the goals of enterprise contact centers…

Customer Service Agent

The most dynamic use case for AI Agents comes in the form of a customer service agent. Before the development of Agentic AI, AI Agents were far less flexible and could struggle whenever a customer strayed too far outside the AI’s pre-defined workflows. 

Agentic AI gives AI Agents a whole new level of autonomy that allows them to freely communicate with customers in 100+ languages, identifying problems and reasoning their way through issues all without having to script or define every possible process or possible conversation flow in advance. 

Contact center owners can simply define the AI Agent’s goals, persona, and job description. From this, the AI Agent can begin conversing with customers, mapping out their goals, and then follow the actions needed to solve the problem.

Outbound Agent

The best example of the flexibility of a more autonomous AI Agent comes in the form of an outbound agent. It is far harder to predict customer patterns and behavior during outbound calls, but Agentic AI can continually adapt and reprioritize while focusing on the overall goal of the call. 

Order Tracking 

Though this is broadly accommodated by the customer service agent use case we’ve covered above, it’s worth noting the specific value of AI Agents in the order/booking tracking process. Queries about existing orders/bookings are one of the most common problems for customer service teams, and resolution often requires a complex, multi-stage process. 

AI Agents powered by Agentic AI excel in this role. Not only can they find a customer’s order based on their details, they’ll also take further unprompted action based on the order’s status and the customer’s history. For example, an AI Agent could track an order number to the warehouse, call the warehouse team to get clarity, then expedite the order and inform the customer of the new delivery timeframe.  

What Are the Use Cases of AI Assistants?

As we’ve already discussed, the common understanding of ‘AI Assistant’ refers to user-facing technology such as Google Gemini and Amazon Alexa. Use cases for those types of tools are more focused on general task management, appointment reminders, goal setting, etc. 

If you don’t need a more autonomous AI Agent, contact centers can instead design and deploy their own AI Assistants for customers or internal teams to interact with. Some everyday use cases include…

Agent Assistance

Non-agentic AI Agents can act as assistants for your human workforce in order to speed up tasks and improve productivity. Cognigy’s Agent Copilot essentially gives your team its own dedicated ‘AI Assistant’ that can provide contextual handovers, transcribe calls, fetch answers, generate content in response to user questions, and even create post-call summaries. 

Self-Service Assistant

Due to the user-focused nature of AI Assistants, they can be deployed to support your customers with self-service tasks. Your company can give customers an interactive, 24/7 source of support that helps them accomplish tasks without having to pick up the phone. 

Just like AI Agents, an assistant can understand input, identify needs, personalize interactions, and carry out tasks – they just don’t have the same level of autonomy as an agent that uses Agentic AI.

In-Store Assistant

An AI Assistant is a perfect in-store guide for businesses that have physical premises. The assistant can offer personalized recommendations to customers, guide them to specific products or areas, and assist them with any queries. This can help you reduce staffing costs and make a customer’s overall shopping experience far more intuitive when compared to having to seek out an in-store human employee.

How Do You Choose AI Agents or AI Assistants?

As AI technology continues to evolve, the difference between AI Agents and Assistants gets blurrier. The only proper separation between them is the presence of Agentic AI, which most people now associate with the term ‘AI Agent’. 

Even though Agentic AI Agents are the latest big development in the industry, they’re not always the ‘best’ option. For many processes, using a more reactive AI Agent (which the public might describe as an ‘assistant) is a better option. We’ve discussed this at length in our guide to the types of AI agents. 

When planning contact center automation, there’s no need to choose between one or the other. By designing a team of AI Agents that are each tailored to a specific role, you can enjoy a ‘best of both worlds’ approach with reactive agents handling simple, deterministic tasks such as ID&V and proactive agents handling complex customer calls.

Cognigy AI Agent Solutions

If you operate an enterprise contact center, you have high demands for any potential AI integration. You need something that can accommodate the dynamic, unpredictable nature of customer service queries, whilst also being able to follow strict, highly deterministic processes. 

Cognigy’s composite AI approach has been specifically designed for the needs of contact centers. Our platform allows you to deploy a team of AI Agents that can switch between reactive, process-driven behavior and Agentic AI goal-oriented proactivity. 

With Cognigy.AI, you can improve service processes, reduce call volumes, improve agent productivity, and unlock massive cost savings as you scale into the future. Book a demo today to see how it works. 

External references

1: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai