What Makes AI Agents Different from Other Types of AI Tools?

Alexey Evdokimov

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Jan 11, 2025

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Let’s clarify the distinctions between buzzwords like AI agents, copilots & assistants, and compare their definitions with a dozen of popular AI tools.

TL;DR

• Existing AI agents are often “one-legged” — they have “tools” to take actions but lack “sensors” to be triggered by external events. Their autonomy is limited for safety, although their reasoning capabilities are nearly advanced enough to enable (semi-)autonomous behavior.
• So far, most AI copilot features in popular tools (except for developer-focused copilots) fall short of enabling deep, user-tailored human-AI collaboration. Most AI tools are merely assistants without user-level memory and true anticipation of user needs .
• ChatGPT is unsurprisingly the most powerful copilot in the market. OpenAI is about to transform ChatGPT into a fully-fledged AI agent. However, its versatility leads to usability issues in specific scenarios, as compared to specialized copilots.

Here’s a highly simplified overview of what AI agents and copilots are:

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Introduction

I’m not aiming to create a new, exhaustive model of AI agent types like

Instead, I’ve conducted thorough research to introduce a much simpler model that answers two key questions:

1. What are AI agents capable of, and what characteristics enable these capabilities?
2. Which types of AI agents aren’t truly agents? Which of these capabilities and characteristics are more aligned with AI copilots and assistants — their “predecessors”?

Based on this model, I will also present my insights into a more practical question:

3. What gaps exist between current AI market offerings and the conceptual answers to the first two questions, which were based on opinions of AI experts and visionaries? (refer to section 5)

1. Why Does It Matter?

• If you’re simply using AI, you might struggle to grasp what the buzzword “AI agent” actually means in your context. You may also wonder about the differences between AI agents, copilots, and assistants — just to better understand the kind of AI tool you’re using at work or for personal advantage. Feel free to skip ahead to the Agentic AI Features section.
• If you’re an AI power user or responsible for AI implementation in your company, you’ll likely want to know what new AI tools to look for and what to expect from them.
• If you’re working at an AI startup, you should understand the actual positioning of your product and be aware of market trends that could impact it.

The year 2025 is widely seen as the moment when AI agents will become enterprise-ready and well understood by the market. Moreover, this is definitely a long-term trend:

• The widespread use of AI agents is perceived as one of the most significant expectations from AI in the coming years, according to Andrew Ng, Google, and other leading organizations:

• The AI agent market is predicted to grow by 45% annually, reaching $47 billion worldwide by 2030:

• Microsoft CEO Satya Nadella has even suggested that agentic applications could replace traditional SaaS.

Nevertheless, you shouldn’t rely solely on these trends and statements if the term “AI agent” still feels vague to you.

2. AI Agent Definitions

The most widely accepted definition, from Gartner Innovation Insights (April 2024), states:

AI agents are autonomous or semi-autonomous software entities that use AI techniques to perceive, make decisions, take actions, and achieve goals in their digital or physical environments.

This definition highlights five key abilities of an AI agent (bolded above), with autonomy being the distinguishing factor that sets AI agents apart from other software possessing similar capabilities.

Nevertheless, the definition above overlooks certain features that enable these core abilities. MarketsandMarkets highlights two additional high-level characteristics in its definition:

AI agents … operate within specific environments, interfacing with users, systems, or other agents, and are characterized by their capacity for adaptive learning, context-aware processing, and autonomous function across varied applications.

• Context-aware processing means that an AI agent’s behavior adapts based on the context, which include both environmental conditions and the history of interactions with a user.
• Adaptive learning means that an AI agent should have memory and the ability to determine what to retain there in order to improve its behavior over time.

Interaction with other agents is another crucial ability included to that MarketsandMarkets definition. AI agents are not limited to interacting with a static “environment” on behalf of a user — they can also form dynamic multi-agent systems. Such system’s capabilities go far beyond what a single agent can do.

Many sources highlight the most fascinating reason why autonomous agents matter: they have the potential to function as employees and coworkers. I would add that their ability to collaborate with other AI agents paves the way for human-AI teams — where AI can participate in teamwork with human-like collaboration.

3. AI Agents vs. AI Workflows vs. AI Copilots

To be considered agents in practice, AI-driven software entities don’t need to be fully agentic — i.e., possessing all the capabilities and features outlined in the definitions above. For instance, some may function as semi-autonomous agents, equipped with memory and goal-driven decision-making, yet lack external tools and sensors or the ability to interact with other agents (refer to section 5 for specific examples).

At present, the line between agents and other “AI tools” is not drawn in a universally accepted way. In reality, this distinction is not a clear-cut line but rather a complex boundary in a multi-dimensional space, where dimensions include decision types, action types, and other capabilities from the definitions.

Let’s consider several differences and then simplify that multi-dimensional space into a clear, comprehensible 2D model.

3.1. Business Perspective: AI Workflows and Agents

One of the less obvious dimensions comes from a recent (Dec 2024) article by Anthropic:

It distinguishes between AI workflows, where LLM calls serve as elements of a predefined process, and AI agents, where an LLM dynamically directs the process.

As a person responsible for implementing AI tools in my company (SMB), I find even simple AI workflows exceptionally valuable. However, while they bring significant benefits to the team, they also introduce new pains for me and other workflow developers. That’s why I look forward to the evolution of agentic AI platforms that could be used to relieve those pains.

While the architectural distinction by Anthropic is useful in enterprise applications, many other perspectives can be used to differentiate agents from other software entities.

3.2. Personal Perspective: AI Copilots and Agents

From the perspective of an individual AI user who prefers not to be replaced by a fully autonomous AI system 😊, an AI copilot is often sufficient, though an AI agent is often too much of a good thing.

Copilots enhance decision-making by offering context-specific recommendations and work collaboratively with humans. Numerous sources support this view, including Microsoft Learn and a more in-depth article written by a data scientist:

I would also highlight that “working side-by-side with a user” (© Microsoft) goes beyond a single iteration of problem-solving. It involves multiple iterations, and this iterative collaboration is what makes true AI copilots different from simple AI assistants.

To gain a deeper understanding of the concept of an AI copilot, let’s examine the most widely recognized capabilities of AI agents:

1. Autonomy — the ability to act independently without direct human guidance.
2. Goal-oriented behavior — achieving broader objectives, not just completing isolated tasks.
3. Environmental interaction:
   a) Perception — gathering external events, through sensors.
   b) Actions — executing tasks outside, through tools.
   c) Data retrieval — accessing information from external sources.
4. Learning — memory and the ability to determine which information is worth retaining in memory. It’s great if a user can manage the memory.
5. Proactive behavior — initiating actions based on triggers rather than merely responding to user requests.

My online research has revealed that AI copilots have the last two capabilities, including the ability to anticipate future user needs (5) based on context awareness and learning (4), as well as information retrieval (3c). This enables a more collaborative relationship between humans and AI copilots, as compared to AI assistants, which are the least capable of the three types of AI tools.

Capabilities 1 (autonomy) and 2 (goal-oriented behavior) serve as the core differentiators of AI agents. The agentic capability 3 (environmental interaction) often means more than simple information retrieval on demand than can be seen in basic AI assistants. It also includes taking actions in the environment via tools and perceiving the environment through sensors.

These sensors enable external triggers to initiate agentic behavior, whereas AI copilots are only triggered by user actions.

4. Agentic AI Capabilities and Features Chart

All of the above leads us to the following “agentic capabilities model”:

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While some might disagree with specific blocks of this “framework”, the core divisions are undeniable:

1. AI Assistants are passive LLM-based processors of user requests. Much like human assistants, they do not work on a task unless explicitly instructed to do so.
2. Copilots are advanced assistants for deeper collaboration with a user on specific tasks and artifacts. Copilots can proactively suggest what you need, even without explicit instructions.
3. Agents incorporate the capabilities of assistants and, in some cases, copilots. They also leverage their own “agentic” features — such as tools and sensors — to achieve goals autonomously, rather than collaborating with the user.

The most perplexing component of the above chart could be memory, because many sources describe memory as a feature exclusive to AI agents rather than AI copilots.

• For instance, in this article by Rezolve.ai, even data retrieval via RAG (Retrieval-Augmented Generation) is associated with agents rather than copilots or assistants, likely as an effort to market their product as an “AI agent.”
• In contrast, Mustafa Suleyman argues that copilots should also have long-term memory at the user level.

• I would add that a deep understanding needs of a concrete user — which is crucial for a true copilot — is unattainable without memory. I mean that an LLM cannot effectively process the entire user’s interaction history unless the most significant insights from those interactions are automatically stored in some form of memory.

Many B2B companies do not even consider the AI copilot concept, as the needs of individual users are not their primary concern. Thus, they typically attribute the memory feature to AI agents, while other copilot features are either downplayed or entirely omitted. For example:

Let’s now map specific AI tools to the components of the above chart.

5. How Many True Agents and Copilots Are Among Widely Used AI Tools?

In addition to capabilities and features, software tools also vary in their level of versatility, i.e. whether they are specialized or not. Some are specialized to serve vertical markets (e.g., education, retail), others are designed for specific business functions (e.g., marketing, customer support) or address particular customer needs (e.g., content generation, translation, question answering, entertainment), and so on.

The completeness of different AI feature sets varies depending on the degree of versatility a tool offers.

5.1. The Most Popular AI Tools

Many of the most popular AI tools (as of August 2024) are specialized in a specific need or task. Examples include text improvement (Grammarly, QuillBot), text-to-speech (ElevenLabs), text-to-song (Suno), image editing (Canva), and even such narrow task as background removal (Remove.bg, which holds almost 2% of the AI market share 🤷🏻‍♂️). Many of these tools are less advanced than even the most basic AI assistants:

• They do not retrieve data from external sources.
• They do not take user context into account, except for a single text or image.
• They merely perform specific tasks, much like traditional software, but leverage specialized AI.

Such software as DeepL, Luma, and CapCut can indeed be classified as AI tools, but only in the most basic sense. There is no place for them in my chart of agentic AI capabilities.

Less specialized products among the most popular AI tools tend to be AI assistants; they are able to process complex contexts.

• AI assistants like Character.ai and JanitorAI apply very limited constraints on versatile LLM capabilities. However, these character generators are specialized in serving users’ creative needs, so I wouldn’t put them in the same category as the non-specialized tools described in the next section.
• The same consideration about versatility apply to Perplexity assistant because web search-based question answering is a narrow user need although being extremely popular.

5.2. Examples of Advanced Versatile AI Assistants

The most versatile AI tools include ChatGPT, Gemini, Claude, POE, alongside numerous emerging alternatives. These tools allow users discuss any topic with an LLM, offering such features as file handling, web search, access to external knowledge bases through RAG, customizable personas (multiple system prompts), prompt templates, etc.

• The leading platform in this category — ChatGPT — is a fully-fledged AI copilot due to its Canvas and Memory features.
  Some other ChatGPT’s features also extend beyond typical assistant functions, particularly if considering GPTs (with actions/tools and custom instructions) and the recently announced Operator (with sensors) as part of the platform.
• Claude.ai is a high-quality yet simple AI copilot, and Claude Artifacts streamlines the editing of short documents, web apps, and other content (this reflects a core copilot use case). Claude lets you choose a style, it’s a kind of “persona” but not customizable.
  Claude Projects offers a searchable knowledge base of files and customizable instructions aka system prompts.
  Unlike GPTs, Claude is not an agent builder by any means, lacking the tool integration capabilities. NB: Claude Computer Use enables AI agent development but targets developers rather than Claude.ai end users.
• TextCortext is an example of the many valuable yet less popular AI assistants. It effectively implements both knowledge bases (files) and personas (system prompts) — essential features for any versatile AI assistant. In TextCortext’s free plan, both features are fully available, with only the number of personas and knowledge bases being limited.
• NotebookLM, a powerful AI assistant from Google, lacks personas, but excels in knowledge base integration, RAG capabilities, and user experience. Users can incorporate both files and Google Docs, view source documents used for each response, and easily exclude irrelevant sources.
• Finally, Dify is an underappreciated AI tool, in my opinion. This capable assistant builder integrates knowledge bases from Notion, websites, and files. Dify includes numerous apps comparable to GPTs. I’d recommend using it for personal benefit. Moreover, Dify also functions as an AI workflow builder — a B2B-oriented tool category that deserves a separate article to review.

These examples demonstrate ChatGPT’s leadership not only in market share but in advanced capabilities. It stands as the only platform among those mentioned that features Memory—an essential copilot function.

However, versatile AI tools cannot yet function as agents (and maybe they shouldn’t?); only developers can create specific agents using OpenAI API or Claude Computer Use API.

While OpenAI’s o1 model is already capable of reasoning at the level required by a basic agent, tool-powered GPTs currently lack o1 integration. OpenAI is developing ChatGPT’s agent capabilities but has delayed release due to safety considerations. A fully-featured OpenAI agent will likely emerge soon, though with intentionally restricted autonomy to remain safe.

In my view, only specialized AI agents are safe enough for mass market, and only specialized AI copilots are easy enough for widespread adoption.

BTW this article presents reasons why developing specialized AI copilots makes sense.

So, let’s examine the capabilities of specialized AI copilots and “agents” currently available.
Spoiler: most of them currently lack a mature feature set.

5.3. Is Microsoft Copilot a Copilot?

I will begin with specialized tools “for everyone.” Microsoft Copilot, similar to Microsoft Windows, represents an attempt to claim ownership of the generic term “copilot,” which carries a different meaning. Moreover, MS Copilot is a set of multiple distinct tools rather than a single unified platform:

• The copilot.microsoft.com web platform does not qualify as an AI copilot in terms of its functionality. Even its assistant-level features are pretty basic, lacking support for non-image files, knowledge bases, and personas.
• Microsoft Copilot app for Windows is a rare example of a personal AI agent with limited autonomy and proactivity. Through deep integration with the OS and Microsoft apps, it executes “magical” actions upon request.
• Microsoft 365 Copilot serves as an AI assistant for PowerPoint, Outlook, Teams, and other MS applications for work. We can label it “AI copilot” just like we label Claude so, though both tools lack the manageable memory feature needed to improve their behavior over time and tailor it to long-term needs of a user.
• Microsoft Copilot Studio is an AI agent builder for enterprises seeking to extend M365 Copilot with their own data and scenarios.

In comparison to Claude Artifacts, Microsoft 365 Copilot takes a more specialized approach, which is advantageous for a copilot.

By focusing on specific workplace tasks (such as creating and editing slide decks), M365 Copilot demonstrates an ability to anticipate user needs to some extent. However, the underlying mechanics remain unclear. I also haven’t found whether Microsoft is developing user-level memory to enhance anticipations across PowerPoint, Excel, Word, and Outlook. I guess that M365 Copilot’s predictions rely only on the current file contents and aggregated data from its vast user base.

5.4. Examples of Specialized AI Copilots and Agents

Existing fully-featured AI copilots demonstrate even more specific specialization than M365 Copilot.

Consider the following examples of copilots and agents from Education (a vertical market significantly transformed by AI), Software Development, Marketing, and Customer Success (business functions):

• Monsha is as good example of an AI copilot for teachers. Iterative collaboration on lesson plans, tests and other such artifacts relies on a general “feedback prompt” for full regeneration of an artifact. While it lacks the change-tracking capabilities of ChatGPT Canvas and appears to operate without memory, Monsha stands out as the most sophisticated copilot functionality among numerous AI assistants for teachers.

If you’re developing an AI product for the education sector, please note that users can significantly reduce time spent reviewing AI-generated materials when regeneration changes are clearly visible, eliminating the need for multiple complete readthroughs of lesson plans, handouts, etc. 🤷🏻‍♂️

• Cursor, GitHub Copilot, and similar development tools represent the most sophisticated implementation of the AI copilot concept, offering deep collaboration without autonomous agent-like behavior. Unlike less advanced copilots, they enable targeted improvements only to selected code snippets and show what exactly was changed.
  Though distinct “personas” aren’t necessary for specialized applications, these development copilots incorporate custom AI instructions within their system prompts.
  While their “memory” capabilities aren’t yet user-configurable, they excel at user intent anticipation and proactive suggestions because they intelligently search through project codebases and add the code found into the LLM’s context.
• Agentforce for Marketing delivers semi-autonomous agents integrated within the Salesforce customer data platform. While costly, it represents an advanced solution, as Salesforce has placed its bets on AI agents. Leveraging comprehensive enterprise data, these agents can create end-to-end marketing campaigns. The platform supports no-code development of custom agents. Though it operates in the B2B space where extensive copilot features aren’t essential, it maintains copilot-like functionality through step-by-step artifact generation and user feedback at each step.
• Intercom’s Fin bot functions as a customer success AI agent, interacting directly with customers to address inquiries and resolve issues. Beyond well-implemented assistant capabilities, Fin stands out through its ability to execute actions on customers’ behalf — an essential characteristic of true agents. Furthermore, Fin’s AI extends to its analytics layer, that’s also a unique feature among competing platforms.

AI agents designed for vertical markets are expected to dominate 2025, according to some experts.

However, the technological gap between (1) advanced platforms like Cursor or Agentforce and (2) the relatively basic vertical AI solutions (for education, healthcare, and other sectors) remains huge and unlikely to bridge in the near future.

This disparity, though, presents an opportunity for innovators looking to develop next-generation AI solutions for vertical markets.

Conclusion

In this article, I have compared the theoretical capabilities and features of AI agents and copilots with the concrete software products currently available in the AI market.

It comes as no surprise that the most advantageous features are found in:

1. ChatGPT and a few other versatile copilots developed by market leaders,
2. Specialized copilots for software developers.

Other niche AI solutions cannot yet be considered “true copilots” at this stage.

When it comes to agentic capabilities, there are a lot of them, but each “AI agent” focuses on a specific subset of these features. As a result, it is not yet feasible to categorize such agents into a “most fully featured” group or a “less advanced” one. Moreover, “truly autonomous AI agents” do not exist at present due to safety concerns.

The research also revealed significant gaps between the capabilities outlined by AI visionaries (those represented in my chart) and the features of the current market offerings.
For instance, most AI copilots lack user-level “memory” to effectively anticipate the needs of individual users. Similarly, the majority of AI agents lack “sensors” to enable proactive behavior.

While this might seem disappointing for us as users, it could represent a significant opportunity for startup founders.

I hope my findings can help you make at least your personal decisions when looking for suitable AI tools.

If you are a product marketing manager or a startup founder,

• You can use the chart and examples to align the core features of your B2C or B2B2C AI product with its market positioning — whether as an assistant, copilot, or agent.
• You may even find new market opportunities to refine your product development roadmap.
• Additionally, there is another valuable chart for you: Success and Failure Factors for LLM-driven Products.

When it comes to B2B AI products and internal AI-driven systems within enterprises, alternative ideas — such as the concept of AI workflows mentioned in section 3.1 — are often more relevant than copilots.

As a product manager primarily focused on the development of B2B products, I plan to share my findings on these types of AI tools in 2025. Stay tuned! 😉