AI Copilots vs AI Agents vs AI Workflows

AI products all use AI models, whether it’s an LLM or a multi-modal model, to power their features, but the interface for how users interact with these models can be vastly different. In the current AI landscape, the 3 types of interfaces we’ve seen from our experience working with AI companies are:

1. AI copilots

2. AI agents

3. AI workflows

It can be difficult determining which interface is the right one for your product. In this article, we’ll provide a framework for thinking about each design, their implications on users, and when each design should be applied.

TL;DR

• AI Copilots are a type of product/feature that work alongside end-users to assist them with their work

  • Use the copilot approach when your product is offering more suggestions than actions - useful for use cases where a human touch is needed for personalization, expertise, AI inconsistency, etc.

• AI Agents - rather than assisting with work - perform work on an end-users’ behalf

  • Use when your AI product can perform work with low-touch where your users can trust your AI application in performing actions for users rather than suggestions for users

• AI Workflows are an interface that allows end-users to define what actions AI Agents should perform and when they should perform them

  • Use when your product is designed to automate work, but needs users to constrain AI Agents to specific tasks within a pre-define series of actions

AI Copilot

AI Copilots are a type of implementation where your AI product works alongside your users, offering suggestions and information to boost their productivity. The benefit of working “alongside” users rather than just performing work for them is that there are many tasks that require heavy human touch that AI isn’t quite ready to completely automate.

For example, when Github Copilot first came out, their AI Copilot introduced the best version of auto-complete, suggesting edits based off the context of the existing code.

Gmail’s Gemini will offer suggestions for emails where users can add/edit for personal touch. This is important for use cases like emails for outbound sales where users would definitely want to review AI outputs before sending.

Use Cases

AI Copilots fit the bill when the problem space your users are in aren’t ready to be completely automated with AI or a human touch is preferred over AI. Tasks that fall under these criteria are:

• require personality and human touch

  • tasks like personalized emails or storytelling

• require personal expertise

  • for large legacy code bases, engineers may prefer an AI Copilot over an AI Agent that writes most of the code (however better models and products have made AI Agents progressively better at this type of task)

• sensitive to mistakes

  • when writing a legal contract, lawyers may want AI Copilots to assist them with research rather than write the actual contract

AI Agents

AI Agents don’t aren’t just offering suggestions and information to your user; they take it a step further by performing work on a users’ behalf. The line between AI Copilots and AI agents can be blurry - at what point is an AI application more of an AI Agent over an AI Copilot?

To put it in a different way,

• if your AI application is more so making suggestions over performing actions → AI Copilot

• if your user is more so directing your AI application to perform actions → AI Agent

I think Cursor’s framing puts it well, where the Cursor is designed for Agents to write most of the code and the user can think like an architect, designer, and PM to guide Cursor. This is a different way of using AI compared to coding AI Copilots.

Because AI Agents are more action-oriented and can even perform work in the background without any user intervention, let’s go over the concepts of tools and triggers.

Tools

Tools are how AI Agents are able to perform work. These can be implemented by your team, provided by an MCP, or provided by an MCP compatible API like ActionKit.

Tools can be as defined for tasks as simple as arithmetic (this was popular when LLMs were surprisingly bad at basic math). Nowadays we see tools more used to interact with APIs so that AI Agents can interact with external systems around them. The most popular API tool is probably the Web Search tool that allows AI applications to search the internet for more context.

Taking APIs further, 3rd-party integrations within your product allow your AI Agents to call APIs and do work on your users’ behalf. For example, tl;dv’s agent has tools like Jira Create Story or Asana Create Task that it can use to extract tasks from a meeting and create them. (If you’re interested in learning how tl;dv has used an integrations platform to ship features like this, read our case study).

Now that we discussed how tools enable AI Agents to perform actions, we’ll discuss how triggers enable AI Agents to perform actions on-demand vs in the background.

Triggers

AI Agents have different triggers to initiate agentic actions. The most straight forward way to trigger an AI Agent is via prompting. For example in Cursor, I can prompt the Cursor coding agent to “install parcel to bundle my project,” triggering Cursor to call its code generation tool and CLI commands tool.

A second way to trigger AI Agents is with event-based webhook triggers. You may want your AI Agent to start performing work on a users’ behalf when an event happens in your application or in an integrated 3rd-party. Using an example like Scratchpad (a Salesforce tool that automates updates and enforces hygiene), their AI Agent updates fields in Salesforce whenever a meeting concludes.

Use Cases

Because AI Agents need to have a high level of trust from users to perform work on their behalf, use cases for AI Agents include:

• routine tasks that don’t need a personal touch

  • meeting assistant agents like otter and fireflies can summarize meetings; customer service agents like intercom can answer customer questions in real-time

• tasks that aren’t mistake sensitive

  • for coding agents like Cursor, v0, or Replit, you can always rollback code edits with version control

• simple tasks that you can trust AI to handle

  • this boundary will undoubtedly change as models improve; in the future, many AI Copilots can transition to Agents as user confidence increases with model performance

AI Workflows

AI Workflows are a type of AI product implementation that gives users more granular control over when and where AI capabilities are used. Unlike AI Agents - AI Workflows don’t give an AI Agent the ability to formulate their own plan or sequence of actions. AI Workflows are designed for users to define deterministic sequences where AI is used for specific tasks.

Because AI Workflows are used repeatedly in the background (using event-based triggers discussed previously), users need to be confident that AI capabilities are used as expected. With AI Workflows, users can define a task for an AI Agent and control what data is being given to an Agent and what data should be outputted.

For example, Copy.ai’s AI Workflows enable users to define steps in a workflow that use AI in a contained, specified way. AI capabilities are given limited data (lead information) and is only asked to complete a specific task and output data for the next step.

Integrations with 3rd-party platforms that your users are accustomed to are really powerful here, such as integrations with CRMs and messaging platforms. They allow your AI Workflows to listen on external events (such as “lead created”) and perform work/push data into external platforms as well (such as ”Send Teams Message,” “Create a Notion Page,” “Send a Gmail”).

If you’re interested in building an AI Workflow feature, our team has a tutorial with sample code on the best way to get started with xyflow’s component library and ActionKit for 3rd-party actions.

Use Cases:

As you can probably tell, the concept of automation and control have come up frequently. AI Workflows work well for use cases that are:

• routine processes that can be broken down into discrete steps

  • can be more complex than tasks for AI Agents, as users can add steps and AI instructions

• tasks that require user touch but are able to be automated

  • good middle-ground between AI Copilots and Agents

  • adds predictability for tasks that that may be too general for an LLM to one-shot while providing automation at scale

Wrapping Up

AI interfaces aren’t a one-size-fits-all, so don’t just throw a chatbot in for every use case.

• Use an AI Copilot implementation for when user interaction is necessary

• Use an AI Agent implementation for simpler tasks and can be done with less user input

• Use an AI Workflow implementation to add predicability and discrete behavior to AI-driven tasks

These interfaces are bound to adapt and evolve as models improve and introduce new capabilities. Even with the changing technology, strategically picking the right interface for users to interact with and trust your AI product will be a requirement now and in the future.