Understanding Agentic AI and Multi-Agent Systems
Imagine a world where technology doesn’t just follow instructions, but adapts, collaborates and solves problems on its own. From self-organizing delivery drones to software managing dynamic supply chains, agentic AI is setting the stage for smarter, more independent systems that are reshaping industries. But what exactly is agentic AI, and why does it matter? Let’s break it down.
Defining our Terms
Before we explore the world of agentic AI, let’s clarify a few key terms:
Agentic: Originating from psychology, this term refers to the capacity to act independently and make purposeful decisions. When applied to AI, agentic systems exhibit autonomy and goal-driven behavior, meaning they can function without constant human oversight.
Agent systems: These are individual software entities or technologies that operate autonomously. They can gather data, make decisions and take action to fulfill specific objectives.
Multi-agent systems: A collection of agent systems working together. These systems are designed to share information, collaborate and adapt to solve complex problems collectively.
Where It All Began
The concept of “agentic” comes from psychology, specifically Albert Bandura’s social cognitive theory. It describes human qualities, such as intentionality and self-direction, or simply the ability to make choices and act independently. Over time, this idea crossed over into the field of artificial intelligence, where researchers began exploring how to design systems with similar autonomous and goal-driven behavior. Dickson Lukose lays out the heritage in this post.
This shift in thinking laid the groundwork for what’s known today as agent and multi-agent systems. These are computational systems designed to perform tasks on their own, often working together to solve big, complex problems.
Understanding Agent Systems and Multi-Agent Systems
An agent system, in simple terms, is a piece of software or technology that operates on its own with minimal human input. It can gather information, make decisions based on that data and act. Multi-agent systems organize multiple agents to collaborate and achieve a shared goal.
A good example is a fleet of autonomous robots that stock warehouse shelves. Each robot independently assesses its assigned tasks, communicates with the other robots and adapts to shifting demands (when new inventory arrives). They don’t wait for step-by-step instructions, and they work as a team to stock the warehouse shelves.
Some key features of these systems include:
Autonomy: Agents operate without constant supervision.
Interactivity: They communicate and coordinate with each other.
Adaptability: They adjust to changes in real time.
Proactiveness: They don’t just react; they anticipate.
How Agentic Technology Elevates Workflow Systems
One of the most exciting uses of agentic AI is in workflow and orchestration engines, which are tools businesses use to manage complex processes. Traditionally, these engines need constant monitoring and manual intervention to deal with unexpected disruptions. Integrating agentic technology into workflows provides a method to incorporate intelligent automation.
Let’s imagine the company that owns the warehouse we talked about before is having a massive sale event. Orders are piling up, inventory levels are fluctuating and shipping deadlines need to be met. Here’s where agentic AI steps in. Intelligent, autonomous agents within the workflow engine can monitor warehouse stock, allocate resources and reroute deliveries in response to a storm causing delays. They adapt without needing manual adjustments, ensuring that operations continue smoothly even in challenging conditions.
This ability to adapt and self-manage doesn’t just save time; it reduces errors and increases overall efficiency. It’s like giving workflows a GPS that reroutes automatically when there’s traffic ahead.
The Road Ahead
Agentic and multi-agent systems aren’t just a glimpse into the future of AI. They are already transforming industries. Already companies use these systems to coordinate fleets of autonomous vehicles, improve healthcare delivery and streamline global supply chains.
Agentic AI is paving the way for a smarter, more adaptable future where technology works seamlessly alongside humans to solve the world’s toughest problems.
That future is closer than you think, but there is lots of work that needs to be done to get our current systems and data ready in order for agentic AI.
Imagine a world where technology doesn’t just follow instructions, but adapts, collaborates and solves problems on its own. From self-organizing delivery drones to software managing dynamic supply chains, agentic AI is setting the stage for smarter, more independent systems that are reshaping industries. But what exactly is agentic AI, and why does it matter? Let’s break it down.
Defining our Terms
Before we explore the world of agentic AI, let’s clarify a few key terms:
Agentic: Originating from psychology, this term refers to the capacity to act independently and make purposeful decisions. When applied to AI, agentic systems exhibit autonomy and goal-driven behavior, meaning they can function without constant human oversight.
Agent systems: These are individual software entities or technologies that operate autonomously. They can gather data, make decisions and take action to fulfill specific objectives.
Multi-agent systems: A collection of agent systems working together. These systems are designed to share information, collaborate and adapt to solve complex problems collectively.
Where It All Began
The concept of “agentic” comes from psychology, specifically Albert Bandura’s social cognitive theory. It describes human qualities, such as intentionality and self-direction, or simply the ability to make choices and act independently. Over time, this idea crossed over into the field of artificial intelligence, where researchers began exploring how to design systems with similar autonomous and goal-driven behavior. Dickson Lukose lays out the heritage in this post.
This shift in thinking laid the groundwork for what’s known today as agent and multi-agent systems. These are computational systems designed to perform tasks on their own, often working together to solve big, complex problems.
Understanding Agent Systems and Multi-Agent Systems
An agent system, in simple terms, is a piece of software or technology that operates on its own with minimal human input. It can gather information, make decisions based on that data and act. Multi-agent systems organize multiple agents to collaborate and achieve a shared goal.
A good example is a fleet of autonomous robots that stock warehouse shelves. Each robot independently assesses its assigned tasks, communicates with the other robots and adapts to shifting demands (when new inventory arrives). They don’t wait for step-by-step instructions, and they work as a team to stock the warehouse shelves.
Some key features of these systems include:
Autonomy: Agents operate without constant supervision.
Interactivity: They communicate and coordinate with each other.
Adaptability: They adjust to changes in real time.
Proactiveness: They don’t just react; they anticipate.
How Agentic Technology Elevates Workflow Systems
One of the most exciting uses of agentic AI is in workflow and orchestration engines, which are tools businesses use to manage complex processes. Traditionally, these engines need constant monitoring and manual intervention to deal with unexpected disruptions. Integrating agentic technology into workflows provides a method to incorporate intelligent automation.
Let’s imagine the company that owns the warehouse we talked about before is having a massive sale event. Orders are piling up, inventory levels are fluctuating and shipping deadlines need to be met. Here’s where agentic AI steps in. Intelligent, autonomous agents within the workflow engine can monitor warehouse stock, allocate resources and reroute deliveries in response to a storm causing delays. They adapt without needing manual adjustments, ensuring that operations continue smoothly even in challenging conditions.
This ability to adapt and self-manage doesn’t just save time; it reduces errors and increases overall efficiency. It’s like giving workflows a GPS that reroutes automatically when there’s traffic ahead.
The Road Ahead
Agentic and multi-agent systems aren’t just a glimpse into the future of AI. They are already transforming industries. Already companies use these systems to coordinate fleets of autonomous vehicles, improve healthcare delivery and streamline global supply chains.
Agentic AI is paving the way for a smarter, more adaptable future where technology works seamlessly alongside humans to solve the world’s toughest problems.
That future is closer than you think, but there is lots of work that needs to be done to get our current systems and data ready in order for agentic AI.
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