AI agents vs. AI assistants: what’s the difference?

It’s not if, but when you’ll incorporate AI agents and AI assistants into your work day. AI assistants and agents can both offload repetitive tasks, answer questions, and provide user services, but there are key differences to know.

Understanding AI agents and AI assistants

AI agents and AI assistants both bring automation capabilities to help workers increase productivity, more accurately gather and use data, and scale operations more quickly. There are some essential differences between the two, namely when it comes to the complexity that each tool can reasonably handle. Human users of these tools should get to know the best uses for each and when to apply them.

AI assistants

An AI assistant is reactive, not proactive. It’s useful for managing schedules and performing reactive tasks, like answering common customer questions in a website chatbot format. Siri and Alexa are good examples of AI assistants, requiring prompts or commands to operate. ChatGPT, Claude, and Gemini also fall into the category of AI assistants.

AI assistants only take action when you ask them to, so prompt creation and engineering is an important skill for human users to understand. These assistants are typically powered by large language models (LLMs), a type of foundation model specializing in text-related tasks, and can learn new skills as you fine-tune them. So, you might ask an AI assistant to compose an email, then refine the email draft based on tone of voice, length, audience, and other factors. The assistant will learn to perform that task better based on patterns — in this case, your input or feedback.

AI assistants can typically use multimodal input such as natural language. They can assist with customer service, IT help desk management tasks, code generation, performing research, and drafting content.

AI agents

An AI agent works best in situations where you want more proactive help beyond a simple back-and-forth prompt situation. AI agents are designed to be strategic and act on their own to complete tasks like network monitoring, dataset analysis and reporting, and executing an IT support workflow. AI agents can make decisions, solve problems, and execute tasks without human intervention. 

Rather than assigning a single task as you would to an AI assistant, you can assign a goal to an AI agent. The agent itself can then figure out how to achieve that goal, whether it’s designing or optimizing a workflow or building a reporting function. While also built on LLMs, AI agents may have persistent memory to learn from past tasks and improve for the future, and can access external tools and data sources, in addition to working with other AI agents. Autonomous agents work without human intervention, while semi-autonomous agents require partial human oversight and might have required approvals for certain actions. 

In the busy IT management area, AI agents like Robin by Atera, can solve user issues autonomously without human intervention needed, from intake to resolution and then to relevant optimizations.

Benefits of AI agents vs. assistants

While AI agents and AI assistants are distinct in what they can do, they share some common benefits:

• Increased productivity: AI agents and assistants can automate routine tasks and streamline processes to help human teams 
• Better collaboration: These tools can work together, with agents interpreting user needs and assigning tasks to AI assistants
• Improved integration: AI models continue to get better at integrating multimodal input, like conversation, to create specific responses faster

Common use cases for AI agents and AI assistants

Both agents and assistants are in use today across industries, with lots more potential still emerging. Here are a few popular areas:

Customer experience

You’ve likely witnessed AI assistants in action for customer experience purposes, such as a web chatbot or virtual voice-activated phone menu. AI assistants can provide support across chat, voice, and email and escalate issues that can’t be easily solved. Natural language processing (NLP) and 24/7 availability make AI assistants very useful for customer experience purposes. 

AI agents can provide a deeper level of support to customers, since they can learn and then optimize for continually improved user interactions. An AI agent can solve a multi-step, complex user problem without human intervention.

Healthcare

Busy healthcare teams use AI assistants to automate processes and improve patient experiences. Assistants can answer questions, schedule appointments, send prescription refills, and support self-service medical record access. Healthcare providers also use AI assistants to summarize patient appointments and other large amounts of information.

AI agents in healthcare can streamline complex processes and use real-time sensor data to suggest changes or improvements, such as in the emergency room triage system. Other use cases include optimizing drug supply management and predicting needed equipment maintenance.

IT Operations

AI assistants in use in IT operations and help desk management serve as the first line of support for users, helping free up time from human technicians. Atera’s AI assistant technology reduces the number of help desk tickets by providing automated support and troubleshooting for common issues. With faster service provided with AI assistants, IT teams can cut down their ticket backlog and get automated ticket and remote session summaries to save time.

AI agents in IT management serve as essential parts of support workflows. Agentic AI can automatically resolve tickets and manage endpoints, freeing up a lot of technician time and making it easier to optimize workflows.

Choosing AI agents vs. AI assistants

As these technologies continue to mature and grow in adoption, you’ll have more and more opportunities to decide when to use which option. Here’s more detail on choosing AI assistants vs. agents.

When to use an AI assistant

AI assistants can be useful for human interactions, such as placing orders, answering simple questions, or asking for help. AI assistants can now process and generate information across multiple data types, like text, images, audio, video, and more. Consider how you might reduce repetitive tasks for human teams by creating a self-serve assistant for employees or a chatbot or voice order system for customers, or choosing a platform that includes the kind of AI assistant useful for the business. 

Remember that assistants can easily make errors if a prompt changes or is unclear, so pay attention to the details in training and prompting AI assistants. A good, useful AI assistant will require user input and fine-tuning to keep it up to date and accurate. Tuning prompts can give the models the necessary context to keep getting better at helping users. In addition, AI assistants don’t automatically retain past interaction memories, since the models themselves don’t evolve based on usage.

When to use an AI agent

Once you’re comfortable with using AI assistants, AI agents might be the next step to offload more complex tasks autonomously. After they’re initially set up, AI agents can work independently without continual prompting like AI assistants. They can develop workflows based on assigned tasks, so using AI agents is a way to build up a digital workforce that removes repetitive work from human teams. Their persistent memory capabilities help agents learn over time to become more efficient and more context-aware.

Consider, though, that agents can get stuck in loops, chasing dead ends and therefore wasting resources. While they act independently, they still need regular oversight. And, AI agents require more compute power to run than assistants do. AI agents can integrate and work with multiple tools in the larger environment, so it’s important to make sure they’re still working accurately if any of those tools change.

Incorporating AI agents and assistants into your work 

AI assistants can perform basic tasks with human prompts, filling the role of reactive helper. AI agents work proactively once they’re trained and set to work on a particular goal. Multi-agent systems are maturing and can work across inputs and tools. Both AI agents and AI assistants play key roles in enterprises today, and there’s still lots of room to explore and experiment to build independent workflows and support humans in doing more strategic work. 

Atera’s IT management platform incorporates both AI agents and AI assistants to serve internal IT departments and managed service providers that need to automate typical tasks. Atera’s core AI functionality is agentic, serving as an always-on virtual technician to support end users. This AI agent, called Robin, diagnoses issues and fixes them on the device without a human technician needed. Plus, the agent can detect and fix IT issues early, before a human even notices.

Meanwhile, Atera’s AI assistant Copilot acts as an always-on assistant to busy IT technicians. It can provide insights and alerts for devices, create custom scripts, and generate summaries of remote sessions and help desk tickets, plus provide ticket sentiment. Copilot saves teams time, speeds up productivity, and can cut down on ticket resolution and backlogs.

>> Learn more about how your IT team can benefit with help from Atera.