AI Copilots vs AI Agents: Understanding the Difference and Choosing the Right Approach

The hype around generative AI isn’t slowing down. In fact, it continues to grow. Did you know that of the roughly 359 million companies worldwide, 280 million use AI in at least one of their business activities? With the growing adoption of generative AI, its market volume is expected to grow to nearly USD 442.07 billion, with a 36.99 percent CAGR from 2025 to 2031.  

However, the growth of generative AI isn’t exactly linear. Different organizations are approaching the use of AI in their own unique ways based on their individual use cases. The result is that two main approaches have emerged: AI copilots and AI agents.  

While both approaches can improve your workflows, the question is not about which one is best. Rather, it’s about figuring out which one works best to solve your specific challenges. 

In this article, we’ll compare both AI solutions by looking at their features, uses, and considerations to help you choose the best option for your needs. We’ll also discuss how Domo can assist you in developing autonomous AI for your business. 

At a glance: Comparing AI copilots vs AI agents 

Let’s explore how AI copilots and AI agents differ in their capabilities, autonomy, and ideal use cases.  

Let’s break down both AI approaches in further detail, starting with why AI copilots are the go-to collaborative assistant. 

The collaborative assistant: Understanding AI copilots 

A copilot is a collaborative AI-powered assistant that works alongside you, offers insights, and helps you be more productive and creative. While they use AI models to process information and provide real-time support and contextual guidance, they do not operate independently.  

Here are some key capabilities and features of AI copilots: 

• Assisting: Copilots help enhance your skills in different tasks by taking care of repetitive or mechanical parts. They save time by quickly analyzing information and improving the efficiency of activities like writing, coding, or managing data. 

• Collaborative partnership: They work as a digital teammate, learning from your inputs to support your decision-making. AI copilots also help refine ideas, analyze trends, and provide suggestions you might not have considered. 

• Human-centric design: AI Copilots are built to augment human expertise, not replace it. They offer an intuitive and user-friendly experience through conversational text and make it accessible without extensive training or technical knowledge. 

• Context sensitivity: Copilots adapt to their environment to maintain their functionality relevant to the task at hand. This design ensures the support they provide is specific and actionable, fitting smoothly into your workflow. For example, in a text editor, they help by suggesting edits or summarizing documents. 

Real-life examples of AI copilots 

AI copilots are changing the way we work by offering real-time help and suggestions. For example, GitHub Copilot helps you write code by understanding what you’re working on and offering personalized recommendations. In applications like Microsoft 365 Copilot, AI assistants generate content, rephrase text, analyze data, and even suggest formulas or create visuals based on your document. 

AI copilot also engages in real time, responding to questions, providing feedback, and offering suggestions based on the current context. Some copilots, like Snowflake Copilot, use natural language processing (NLP) to translate user questions into queries, like SQL queries to retrieve and analyze data. 

Microsoft 365 Copilot Example Workflow | Source 

AI copilots excel in tasks requiring supervision, but their reliance on continuous input limits their autonomy. For a more independent solution, let’s turn to AI agents, the autonomous executors. 

The autonomous executor: Understanding AI agents 

When comparing AI copilots vs AI agents, agents are a more advanced form of artificial intelligence (AI) that operate independently. They complete entire tasks on their own, handling processes from start to finish like a human worker.  

Unlike copilots, AI agents are not limited to providing suggestions or assistance. They reason and solve problems like you do and take actions all on their own to achieve specific business objectives. They are also capable of running business processes, adapting to new challenges, and improving over time. 

Some key capabilities and features of AI agents include: 

• Autonomy: AI agents take on tasks without needing constant input from you. Once set up, they can run on autopilot around the clock and get things done. This helps you to focus more on strategic work while the AI agent manages routine tasks. 

• Task ownership: AI agents handle entire tasks, not just parts. Whether processing orders or managing reports, they complete the full workflow with little oversight, letting teams focus on more critical work. 

• Consistency: AI agents provide consistent performance without fatigue to ensure high-quality outcomes in routine tasks. 

• Adaptability: They adapt and improve their performance over time by analyzing and processing user interactions and feedback. This ability helps agents to refine responses, personalize user experiences, and become more effective in their tasks. 

• Scalability: Agents can handle both small and large volumes of work. They adjust to increasing workloads without needing additional human resources. 

AI Agent Workflow Example | Source 

Real-life examples of AI agents 

The FordDirect Dealer AI Agent, developed in partnership with OneMagnify, uses the Domo Agent Catalyst to build AI solutions for its customers, including FordDirect. The FordDirect AI Agent provides dealers with easy access to information from 9 to 15 different dashboards, as well as new data that was previously inaccessible. Dealers can ask the AI agent questions using their voice on any device. They don’t need to log into different systems or search through reports.  

This system provides valuable insights into customer behavior, including inventory interest, purchase or service likelihood, and the latest vehicle preferences. It’s useful for roles in sales, demand management, and inventory management, where data is often fragmented and siloed. 

FordDirect is also planning to take its Dealer AI Agent a step further in automation, where it will detect different system events and trigger alerts to the dealership. For example, if the explorer window configurators are running low, it will alert dealers and ask if they want to order more. It can also make recommendations, such as automatically shifting media spend based on inventory levels. 

While AI copilots and AI agents serve different roles, they exist along a continuum of AI enablement. Knowing when to use each is key to getting the most value for your business. 

The copilot–agent continuum: When to use which? 

Choosing between AI copilots vs AI agents comes down to what your business needs and how you prioritize AI automation to boost efficiency. They differ greatly in purpose, from how they interact to how much autonomy they bring. 

Here’s a simple breakdown to help you decide the right option: 

When to use an AI copilot 

Choose AI assistants when you want real-time support while staying in control. They work best as digital companions rather than task owners. Use AI copilots when: 

• Your judgment drives outcomes: Choose copilots when the task requires your intuition, creativity, or ethical perspective. You stay in control, using their suggestions to guide decisions and refine outcomes.  

• Complex decisions are needed: When you are faced with choices that require analysis from different angles, copilots step in with quick insights and recommendations. This helps you make smart, strategic decisions without giving up control. 

• You want flexibility in your day-to-day: Use copilots for tasks like editing reports, building presentations, or refining code. They adapt to your workflow without disrupting it. 

When to use an AI agent 

Unlike copilots, AI agents are your operational executors. They are built to take full ownership of tasks from start to finish, without constant check-ins.  

Deploy agents when: 

• Automation is the goal: Deploy agents when the primary objective is to fully automate routine repetitive tasks. Once set up, they run on autopilot, and you can reallocate your effort to higher-value initiatives. 

• Scaling operations is nonnegotiable: If your team struggles to keep up with rising demand, agents handle high-volume workloads without the having to hire or train additional staff. They process thousands of requests with the same precision and speed every time. 

• Consistency matters: Agents provide consistent, error-free results by following established protocols. This is essential when your operations demand compliance, accuracy, and consistency across all processes. 

Strategic implementation considerations 

Rolling out AI copilots and agents requires a thoughtful approach to maximize benefits and reduce risks. Here’s how you can make the transition smoother and more impactful: 

• Start with easy wins: Begin with standardized, low-risk processes to build trust in AI systems. Focus on single-task roles for initial implementation. For example, use copilots to automate dashboard creation and agents to handle data reconciliation with minimal manual input. 

• Take a phased approach: Introduce copilots first to familiarize teams with AI-enhanced workflows. Once comfortable, integrate agents to automate entire processes end-to-end for maximum impact. 

• Keep ethics front and center: Agents, especially those making decisions without human input, come with risks like bias and lack of transparency. Audit their training data, diversify inputs, and keep humans in the loop where decisions carry weight. 

• Think long-term from the start: Your AI strategy should align with your broader goals. Choose scalable platforms, like Domo, that grow with you, integrate seamlessly with existing systems, and maintain robust governance.  

With a clear understanding of when to use copilots versus agents, let’s examine the value AI agents bring to business intelligence through automation. 

The value of AI agents in business intelligence 

Autonomous AI agents are delivering tangible, bottom-line results across industries such as finance, consulting, customer service, and logistics. For many enterprises, business AI automation initiatives driven by these agents have cut costs by 30–50 percent, while also delivering faster and more consistent operations. 

For example, H&M’s virtual shopping assistant answered 70 percent of customer questions automatically, which increased sales by 25 percent and made resolving issues three times faster. Similarly, Microsoft, with extensive AI integration, saved over $500 million in its contact centers while increasing coding productivity (35 percent of code production and faster time to market). 

Furthermore, about 19 percent of Fortune 500 companies, including Procter & Gamble and Unilever, now fully automate their financial reconciliation tasks using AI agents. These agents automatically check transactions, identify mismatches in ledgers, retrieve supporting document explanations, and complete audit logs.  

Unlike older automation tools, these agentic systems handle unusual cases, adapt to different formats, and escalate issues intelligently, reducing the need for manual work. 

However, success is not guaranteed as Gartner warns that nearly 40 percent of agentic AI projects may fail by 2027, often due to unclear goals, insufficient governance, or rushed implementation. 

To avoid these pitfalls, businesses should have secure, scalable platforms with built-in guardrails. Domo’s Agent Catalyst offers an enterprise-grade framework to design, deploy, and manage AI agents reliably within complex operations. 

Why choose Domo’s Agent Catalyst for building your first AI agent? 

Domo Agent Catalyst supports the entire agent lifecycle with a secure, private foundation built on Domo’s AI platform. It’s engineered to analyze and complete business processes autonomously, while also maintaining security, governance, and human oversight. 

This solution centers on a structured visual framework that simplifies agent creation into four steps. 

1. Select an LLM: Use DomoGPT, a Domo-hosted governed language model, or connect a third-party option. 

2. Provide clear instructions: Define the agent’s mission and expected outcomes. 

3. Connect to knowledge sources: Link data sets, semantic layers, and unstructured data via FileSets. 

4. Assign tools: Enable agents to act within and across systems, using built-in AI Services and custom functions.
   
    

What’s notable is that you can perform these four steps with a no-to-low-code approach. This empowers your organization to reduce agent setup time while reducing the need for complex development pipelines. This is what our customer, RXA@OneMagnify, referred to as “the perfect blend of innovation and control,” highlighting its governance, security, and AI automation capabilities. 

Another advantage of using Domo is that through DomoGPT and role-based governance, every agent operates within an auditable platform. This means your agent actions are traceable, compliant, and aligned with data policies.  

Explore how Domo helps you build and deploy AI agents at scale and go beyond copilots. If you want to learn more about AI, check out our Future of AI video series. 

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