MAKE Issue: AI Agents and Agentic Automation
The Difference Between AI Agents and Agentic Automation
AI agents and agentic automation are terms that are often used interchangeably.
However, these two are clearly different concepts, representing distinct stages of AI evolution.
If you've ever mixed up "AI agents" and "agentic automation," you're probably not alone.
Over the past few years, as AI-related terminology has proliferated, the boundaries between these two concepts have become somewhat blurred.
But agentic automation and AI agents are entirely different concepts.
Think of them as separate terms that describe different things.
With AI continuing to evolve at a rapid pace, it's worth taking a moment to clearly understand these terms.
In this article, we'll explain what each term means, where they overlap, and where they differ.
We'll also show how these terms apply to the broader landscape of AI and automation, and why understanding the difference helps you build more strategic AI workflows by matching the right tool to the right problem.
What Is an AI Agent?
An AI agent is a helper that combines automation with limited decision-making, problem-solving, and real-time interaction with its environment.
It can analyze the current situation, adapt to changes, and determine the best next step, but it's designed to focus on fairly specific tasks.
According to Sara Maldon, Make's Head of AI and Business Automation, an AI agent typically combines these three elements:
- Large Language Model (LLM): Handles language processing and generation
- Context: Data the agent can reference when making decisions
- Tools: Various tools that can perform tasks ranging from simple calculations to sending emails or scheduling meetings
Sara says:
What makes AI agents special is that they're goal-oriented. The agent has some goal set by the user, and because it has these tools and a kind of 'brain' and context, it can execute on that.
In this video from the recent RAISE Summit in Paris, Sara highlights what makes AI agents different from other approaches.
AI agents are not simply 'AI automation' or existing workflows with a model attached.
While these systems can use LLMs, data, and tools, they don't operate with the defined goal-oriented autonomy that characterizes AI agents.
What Is Agentic Automation?
Agentic automation extends the capabilities of individual AI agents to 'swarm'-scale assistance.
Instead of a single agent working toward one narrow goal, dozens or even hundreds of agents can work simultaneously.
Each agent can make decisions, act independently, and collaborate with other agents to achieve broader, more complex objectives.
Think of agentic automation not as just performing tasks, but as having agents that function like complete AI employees or entire teams.
These agents collaborate across processes, departments, and systems to handle diverse tasks, adapt to changes, and produce outcomes that a single agent alone couldn't achieve.
Agentic Automation vs AI Agents
While the two terms are related, they differ in scope and complexity. Here are some key differences:
- Scale: AI agents focus on one fairly specific task. Agentic automation uses many agents to achieve much broader goals.
- Goals: AI agents are defined by a single goal-oriented function. Agentic automation pursues multiple goals across processes or departments.
- Autonomy: AI agents make decisions within their assigned tasks. In agentic automation, decision-making is distributed across the entire agent network.
- Coordination: AI agents operate independently. In agentic automation, agents coordinate and share information to work toward shared outcomes.
- Results: AI agents deliver results for a single process. Agentic automation delivers results that a single agent couldn't achieve alone.
The Evolution of Agentic Automation
The transition from rule-based workflows to agentic automation didn't happen overnight.
It's the result of years of experimentation, new technology, and changing expectations about what automation can do. Here's how it evolved:
1. Rule-Based Automation
The first wave of automation relied on strict 'if-then' rules.
It was predictable but inflexible, since any changes to processes or logic required manual input.
2. Non-Deterministic Automation
With the advent of AI models, workflows could adapt on the fly, respond to new inputs, and make decisions that weren't pre-programmed.
This shift eliminated the need to pre-code every possible outcome.
3. AI Agents
Building on this adaptability, AI agents combined LLMs, context, and tools to autonomously pursue specific goals.
They could determine the best approach to reach a defined outcome without continuous human input.
4. Agentic Automation
In the final stage, multiple AI agents are connected into a coordinated network.
Together, they can handle dozens or hundreds of tasks in pursuit of broader goals, functioning like an AI-powered workforce rather than a single process.
See how these stages build on each other, from rigid rule-based workflows to adaptive AI agents, and finally to agentic automation where multiple agents collaborate as a single team.
Each stage expanded what automation could do, bringing new opportunities and challenges.
Benefits
AI Agents
- Handle specific tasks without constant supervision.
- Adapt to changing inputs and adjust tasks in real time.
- Reduce manual work for repetitive, rule-based, or predictable processes.
- Provide building blocks for scaling toward agentic automation.
Agentic Automation
- Execute multiple processes simultaneously across teams, departments, and systems.
- Distribute decision-making so work can proceed without bottlenecks.
- Deliver complex, multi-step outcomes that would be too slow or expensive to achieve manually.
- Provide flexibility for agents to work independently while sharing information to achieve common goals.
Risks and Challenges
Challenges with AI Agents
- Limited scope: Each agent focuses on one specific goal.
- Quality control: Output still requires review to avoid errors or irrelevant results. Make's visual no-code scenario builder makes it easy to inspect and adjust each step, allowing you to refine agent behavior without writing code.
- Integration gaps: Agents may not seamlessly connect with every tool, system, or dataset they use. Make's library of 2,800+ pre-built apps provides agents with more connection points without complex custom development.
Challenges with Agentic Automation
- Orchestration complexity: Managing dozens or hundreds of agents to work together toward shared outcomes is required.
- Risk of conflicting tasks: Without coordination, agents may duplicate work, increasing costs, or interfere with each other's progress.
- Higher maintenance costs: More agents mean more configuration, monitoring, and fine-tuning needed over time.
- Data and compliance: Multiple agents operating independently must still adhere to security and privacy rules.
Make Grid addresses these complexities by providing a single visual map of your automation landscape, showing how agents, workflows, and data flows connect.
It helps you coordinate more effectively, spot potential issues, and execute large-scale changes with confidence.
IMPAKERS Blog | Make Agent.ai Module Added Read More
Source: Stuart Aitken, Make, "The difference between AI agents and agentic automation", https://www.make.com/en/blog/the-difference-between-ai-agents-and-agentic-automation, (2025.08.21)