AI Is Evolving Fast—Do You Know the Differences?
AI has become central to nearly every business conversation, and AI fluency has quickly become essential. It’s changing how companies analyze data, plan operations, and serve customers. As a result, leaders across industries are urging teams to understand what AI is and how to use it effectively.
But as the technology evolves, the terminology has grown confusing. Terms like LLM, Generative AI, AI Agents, and Agentic AI are often used interchangeably, yet they describe different capabilities.
Understanding these distinctions helps leaders make better investment decisions, assess risk, and manage expectations. Here’s a clear breakdown of what each system does and why it matters for your business.
LLMs (Large Language Models)
A Large Language Model (LLM) is a machine learning system trained on vast amounts of text from books, articles, websites, and code. Its primary function is to predict the next word in a sequence, a deceptively simple mechanism that powers much of what feels intelligent about modern AI.
LLMs excel at understanding context and generating coherent text. They can draft emails, summarize documents, write code, and answer questions. But it’s important to recognize their limitation: they don’t “think.” They recognize and reproduce patterns in data rather than reasoning or acting independently.
Examples: ChatGPT (OpenAI), Gemini (Google), and LLaMA (Meta).
Generative AI
Generative AI is a broader field encompassing systems that learn from data and create new content such as text, images, music, or video. LLMs fall within this category, but Generative AI extends far beyond language.
For example, DALL·E and Midjourney create images from text prompts, while tools like Synthesia generate video content from scripts.
The defining feature of Generative AI is creativity: these systems produce new material that resembles what they’ve learned, but with original variations.
Examples: Midjourney, DALL·E, Synthesia, Runway.
AI Agents
AI Agents are individual, task-oriented systems that integrate other AI models with tools, APIs, and data systems to perform tasks autonomously. These systems can break down large objectives into smaller steps, use the appropriate tools, make decisions along the way, and iterate based on feedback or new data. Instead of just generating text, they take action.
An AI Agent might retrieve live data, analyze it, make a decision, and execute an operation, such as drafting a report, updating a database, or scheduling a shipment. It is capable of completing a multi-step process without requiring constant human input.
Examples: AI-powered customer support bots, research assistants, or workflow automation systems.
Agentic AI
Agentic AI represents the next stage of AI development—systems that coordinate multiple AI Agents to pursue broader goals. These systems plan, reason, and adapt with limited supervision. Where an AI Agent focuses on a single task, Agentic AI orchestrates multiple agents to achieve complex objectives.
Think of it as the difference between a skilled employee and an entire department working in sync. Agentic AI allows for more independent, adaptive, and proactive problem-solving than single AI agents alone.
Examples: Multi-agent systems coordinating logistics, scheduling, or supply chain optimization.
“Agentic AI is like having multiple specialized workers collaborating behind the scenes to solve a problem. You don’t have to keep prompting—it’s already thinking through the next step.”
—Kevin Knowlton, Engineering Manager at Sifted
Key Differences
| Type | Function | Output | Applications |
|---|---|---|---|
| LLMs | Analyze text and predict language patterns | Written content or insights | Text generation, summarization |
| Generative AI | Create new content across media types | Text, images, video, or audio | Design, content creation |
| AI Agents | Execute defined tasks using AI tools | Actions, data, or reports | Workflow automation |
| Agentic AI | Coordinate multiple AI agents toward goals | Multi-step operations | Strategy, systems management |
The distinctions lie in the level of autonomy, purpose, and complexity:
- LLMs are the core technology that generates text,
- Generative AI creates various media types,
- AI Agents use LLMs with tools and memory to autonomously perform tasks, and
- Agentic AI represents more complex, multi-agent systems that strategize and adapt to achieve complex goals.
In simple terms: LLMs interpret, Generative AI creates, AI Agents act, and Agentic AI decides.
Why These Distinctions Matter
Understanding these layers isn’t just technical—it’s strategic for your business. Each type of AI carries different implications for governance, security, and ROI.
- Risk and Responsibility: LLMs and generative models produce outputs but don’t act. Agentic systems, however, can execute tasks within business infrastructure, like accessing data or triggering actions, which introduces new dimensions of oversight and accountability.
- Expectations and Budgeting: Many tools marketed as “AI agents” today are, in reality, sophisticated chat interfaces. Knowing what’s possible (and what isn’t yet) helps companies invest wisely.
- Competitive Advantage: Businesses that adopt agentic systems can automate decision-making, improve operational efficiency, and free teams to focus on strategy instead of repetitive tasks.
Ultimately, understanding AI’s evolution enables smarter decisions, better governance, and more effective innovation.
SiftedAI Copilot: The Agentic AI Advantage
For years, Sifted has led the evolution of logistics technology. Now, we’re taking the next leap with SiftedAI Copilot.
Launching early 2026, SiftedAI Copilot will bring Agentic AI directly into the SiftedAI platform. It doesn’t just answer questions—it routes to systems, executes queries, and recommends the next best move for your network.
SiftedAI Copilot has the context, tools, and data to work for you, not just with you:
Auto Copilot
Works in the background to monitor, detect, and automate logistics workflows so you don’t have to.
“I’ve detected shipments being upgraded from Ground to 2-Day Air unnecessarily. This is adding avoidable costs. Set routing rules to default packages to Ground when delivery time permits.”
Copilot Assist
Your on-demand shipping analyst, ready with answers, insights, and connections to experts.
“Why did my costs go up last quarter?”
Copilot Simulation
A safe space to test goals and strategies, modeling outcomes before making real-world decisions.
“Which mix of service levels would minimize costs while still hitting a 95% on-time delivery target?”
SiftedAI Copilot can predict what’s coming, guide smarter decisions, and automate everyday tasks, all within a secure, unified system built for your supply chain.
This isn’t just another tool. It’s built by logistics and AI experts, backed by a team that understands your business.
Would you like to be notified when SiftedAI Copilot is live? Sign up here.
