If you log onto LinkedIn on any given day, you’re likely to see a flood of buzzwords and bold claims about technology that's "going to change everything." Sometimes those predictions are right—but often, they fizzle. With AI evolving at breakneck speed, even the most tech-savvy professionals can get tripped up by a rapidly expanding vocabulary. Terms like AI Agents, Digital Assistants, and LLMs (Large Language Models, such as GPTs) are often used interchangeably, but they represent distinct tools with very different capabilities—and very real implications for how businesses operate today.
In this post, we’ll break down the differences between these concepts, define where they fit into modern revenue operations, and help you understand which to use—and when.
Table of Contents:
Let's break down the layers of AI functionality from the core tech (LLMs) to tools that act (agents).
What They Are:
LLMs—like GPT-4, Claude, and Gemini—are predictive text engines trained on huge datasets. They don’t do anything on their own. Instead, they generate responses based on what you type. They're like the "brain" of the operation.
How They Work:
You ask a question or give a prompt, they return a response based on language patterns—not memory, intention, or goals.
Example Use:
Typing a question into ChatGPT and getting a paragraph answer
Asking Claude to summarize a long report
Key Traits:
Stateless (unless wrapped with memory)
Zero autonomy
General-purpose and flexible
Think of them as:
A super-smart calculator for language. You input, it outputs. No follow-up unless you prompt again.
What They Are:
Digital Assistants are applications built on top of models like GPT. They give those models a face, a voice, and often access to tools or workflows. They're designed to help you with tasks, but they wait for you to tell them what to do.
How They Work:
They leverage an LLM’s capabilities but add:
Interfaces
Prompts/presets
Light workflow memory
Task support (like scheduling or writing emails)
Example Use:
ChatGPT (with memory & tools)
HubSpot’s Breeze Copilot (formerly ChatSpot) pulling CRM data
Google Assistant responding to voice commands
Key Traits:
Initiated by a human
Task-specific or domain-aware
May handle multiple steps—but still needs direction
Think of them as:
An executive assistant with great tools—but they won’t take initiative unless you ask them to.
What They Are:
AI Agents are autonomous systems that can operate independently toward a goal. They might use an LLM for language understanding, but they add reasoning, planning, and action-taking. Often, they work across tools, APIs, or even with other agents.
How They Work:
You give a goal
The agent breaks it into tasks
It executes (with or without human oversight)
Example Use:
A content creation agent that:
Audits existing blog content
Identifies gaps
Creates drafts
Submits them to your CMS
A sales agent that:
Monitors pipeline status
Sends reminders or follow-ups
Updates CRM automatically
Key Traits:
Autonomy: can act and iterate
Multi-step execution
Integrates across tools/systems
Think of them as:
A freelance specialist who gets the brief and runs with it. You check in, but they make decisions and deliver without needing constant instruction.
The AI landscape isn’t just academic—these tools are already transforming how revenue teams operate. But to put them to work effectively, it’s essential to understand their operational boundaries.
Let’s explore how each type of AI shows up in real workflows across Marketing, Sales, and RevOps:
Below is a breakdown of how each role can use these tools—and what level of support to expect:
| Role | LLM Use Cases | Digital Assistant Use Cases | Agent Use Cases |
|---|---|---|---|
| Marketing | Prompt to draft social post or blog copy | Have ChatSpot generate SEO meta descriptions | Automate full content audit and strategy updates |
| Sales | Draft email responses | Use AI assistant to prep meeting briefs | Auto-follow-up workflows and CRM updates |
| RevOps | Creating the right workflow segments for lead routing | Use assistant to generate a dashboard template | Identifies knowledge gaps in your knowledge base articles and helps write the initial draft |
Choosing between an LLM, a Digital Assistant, or an AI Agent isn’t just about tech preference—it’s about task complexity, risk tolerance, and desired autonomy.
Here's how to decide which to use depending on your situation:
|
Situation |
Use an LLM |
Use a Digital Assistant |
Use an AI Agent |
|
You want help writing or brainstorming |
✅ |
✅ |
🚫 |
|
You need task support with context (e.g. using your CRM) |
🚫 |
✅ |
🚫 |
|
You want a system to fully execute a workflow |
🚫 |
🚫 |
✅ |
|
You want total control with minimal risk |
✅ |
✅ |
🚫 |
|
You want to scale repetitive work |
🚫 |
🚫 |
✅ |
Start with Your Problem
| Type | Tools |
| LLMs | ChatGPT, Claude, Gemini, Mistral |
| Digital Assistants | ChatGPT w/ memory + tools, HubSpot Breeze, Siri, Copilot |
| Agents | agent.ai, HubSpot Breeze Agents, AutoGPT, LangChain Agents, |
Understanding the difference between LLMs, Digital Assistants, and AI Agents helps you build the right AI stack for your business goals. It’s not about one replacing the others—it’s about matching autonomy, context, and control to your needs.
Whether you’re a marketer trying to scale content, a RevOps leader streamlining data workflows, or a sales pro aiming to automate follow-ups, knowing which AI tool to use—and when—will give you a strategic edge.