AI Agents are so 2022, let's talk about Agentic AI

Why the Future of Automation Is Getting Smarter (and More Complicated)

There’s a lot of buzz about “AI agents” right now, tools that can schedule your meetings, summarize your inbox, or handle simple customer questions.

But there’s a bigger shift happening behind the scenes. It’s called Agentic AI, and it’s not just smarter, it’s fundamentally different.

At Romulus Technology, we like to simplify complex things. So here’s what you need to know.

What Is an AI Agent?

An AI agent is a tool powered by a Large Language Model (LLM) that can do basic tasks on your behalf. Think of it as a supercharged assistant:

• It can use tools (like calendars, databases, or CRMs)

• It can remember a bit of context

• It follows a predefined workflow

Useful? Yes.

Autonomous? Not really.

What Is Agentic AI?

Agentic AI takes it further.Instead of a single assistant, you have a network of agents that collaborate, coordinate, and adapt.

Think of it as a smart, self-organising team.Each agent has a role. Together, they break down complex goals, share information, learn from each other, and get better over time.

This isn’t just one tool reacting to a prompt, it’s a system that plans, reasons, and acts across multiple steps.

Real-World Examples

AI Agents	Agentic AI
Auto-replying to emails	Managing and prioritising your inbox dynamically based on sentiment, urgency, and topic coordination
Booking a meeting	Coordinating a 5-person workshop across time zones, availability, and travel schedules
Summarising an article	Leading a multi-source research project with citations, summaries, and recommendations

Why This Matters for Automation

Whether you’re automating marketing, operations, or construction site coordination, you’re building systems that must:

• Handle complexity

• Adapt to changing inputs

• Coordinate between people, processes, and tools

  
  

Agentic AI brings structure to that chaos.

It allows you to:

• Assign roles to digital agents

• Define goals, not just tasks

• Build feedback loops that learn over time

  
  

The Challenges (Let’s Be Real)

Agentic AI isn’t magic. It brings new challenges:

• How do we prevent coordination failure between agents?

• How do we trace decisions made by autonomous workflows?

• How do we keep memory consistent across complex processes?

The answer lies in smart architecture: Better prompting, persistent memory, and orchestration layers that act like a meta-manager for your AI team.

At Romulus Technology, We Think in Systems

Whether we’re designing a process for a human team or a digital one, the questions are the same:

• Who does what?

• What triggers the next step?

• How do we know when it’s done?

Agentic AI just lets us apply that thinking at scale, faster, and with less human intervention.

And we’re building for that future.

Curious how AI agents (or agentic systems) could streamline your business?Let’s talk about it, before the robots do it for us.

Resources : https://www.researchgate.net/publication/391776617_AI_Agents_vs_Agentic_AI_A_Conceptual_Taxonomy_Applications_and_Challenges