A New Chapter in AI: 2025 and Beyond
Have you noticed everyone calling their automation workflows “AI Agents” these days? It’s a catchy headline, sure—but it also muddies the waters. Automation can be incredibly helpful, yet it’s hardly the same as a system that adapts, plans, and learns on its own.
Think of it this way: if automation is a one-trick pony (albeit a useful pony), a true AI Agent is more like a Swiss Army knife—capable of switching tools and tactics the moment it senses changes.
From Toni Dos Santos post https://www.linkedin.com/posts/dossantostoni_dont-be-fooled-by-people-selling-you-ai-activity-7284132009134706688-9cXl?utm_source=share&utm_medium=member_desktop
What Really Defines an AI Agent
Here’s the heart of it: an AI Agent doesn’t merely execute steps in a prescribed order. Instead, it:
- Observes data or your current context
- Plans a strategy, possibly breaking tasks into smaller steps
- Acts in real time, using the right tools, APIs, or resources
- Learns from successes and mistakes, adjusting on the fly
In other words, it does its own thinking. As Anthropic puts it, real AI Agents manage “model-driven decision-making” that can adapt to fresh scenarios—no extra coding required. Meanwhile, NVIDIA’s blog on Agentic AI emphasizes systems that “sense, reason, and act,” urging us to look past basic, rigid scripts.
“A true agent is not just another workflow—it’s an adaptive partner that re-plans the journey if the destination changes.”— Inspired by research from Anthropic’s Building Effective Agents and NVIDIA’s Agentic AI
But Automation Isn’t Useless
Let’s be clear: automation still rocks for simple, repeatable tasks. It’s perfect when:
- You have predictable routines (e.g., sending emails to new subscribers)
- Real-time decisions aren’t required (e.g., invoice approvals under a certain amount)
- The environment changes very little over time
In these cases, a full-blown AI Agent might be overkill. Automations remain valuable, freeing humans from drudge work. They just aren’t “agents” in the sense of advanced autonomy.
Quick Example: Basic vs. Agentic
Scenario: You want to plan a trip next month.
Automation Workflow
- Takes your specified date and airline preference
- Books the flight
- Sends a confirmation email
AI Agent
- Checks dynamic factors: flight prices, weather forecasts, loyalty points
- Suggests itinerary changes if storms are likely
- Books alternatives if your initial airline is suddenly canceled
- Tracks your seat preference from past trips and automatically picks the same seat type
Notice how the AI Agent is ready for twists and turns, while the automation workflow follows a single path and halts if any unexpected event pops up.
Why Everyone’s Rushing Toward Agentic AI
Major players in the AI world believe agentic systems will reshape how we tackle complex tasks:
- Anthropic: They’re refining frameworks like ReACT (“Reason+Act”) for multi-step reasoning.
- NVIDIA: Their “Agentic AI Blueprints” push for real-time adaptive solutions in industries like robotics and transportation.
- IBM Research: Uses “Retrieval-Augmented Generation” (RAG) to keep AI updated and accurate rather than stuck in stale training data.
Whether it’s advanced customer support, supply chain optimization, or trip planning, the promise of an AI Agent is that it learns and evolves without the need for constant reprogramming.
Looking Ahead: What to Expect in This Series
In the weeks to come, I’ll explore:
- Designing Safe Agents — how to prevent “rogue” automation that has too much freedom.
- Combining RAG & ReACT — bridging real-time data with multi-step reasoning.
- Real Case Studies — success stories and pitfalls in applying agentic AI across finance, travel, and more.
- Ethics & Society — ensuring these agents truly serve humans without bias or misinformation.
By the end, you’ll spot the true difference between “just another workflow” and an agent that can genuinely collaborate with you—whether you’re scheduling a dentist appointment or optimizing global logistics.
Final Word
So the next time you see “AI Agent” slapped on a simple sequence of tasks, take it with a grain of salt. Automation is incredibly helpful, but it doesn’t magically become “agentic” unless it can observe, plan, and adapt. As we ride into 2025, keep an eye out for the real deal—and don’t settle for anything less than an AI that thinks for itself.
I recommend watching this video by Maya Murad from IBM Research, which provides valuable insights into Agentic AI.
References
- Building Effective Agents (Anthropic)
- Computer Use for Agents (YouTube, Anthropic)
- What is Agentic AI? by Maya Murad, IBM Research (NVIDIA Blog)
- RAG (Retrieval-Augmented Generation) (YouTube, IBM Research)
- The ReACT Framework for Intelligent Agent AI (AskUI Blog)