AI Agents are autonomous AI systems that can reason about problems, decide which tools to use, take actions across multiple steps, and iterate on their work without human intervention at each decision point, moving beyond simple copilots to become active participants in business workflows.
The term "AI agent" has become the dominant narrative for 2025, yet most explanations are either too technical or too simplistic. Understanding what makes an AI agent different from the AI tools you're already using is crucial for business leaders planning their next phase of AI investment.
When I work with clients to explain how do AI agents differ than other AI imlementations, I use a simple three-level framework to clarify what we're actually talking about:
Level 1: LLMs and Copilots - humans do the tasks
You use ChatGPT to draft an email or Claude to summarize a document, but you're still doing the work - providing prompts, reviewing outputs, and deciding what to do next. The AI assists you, but the human is firmly in control at every step.
Level 2: AI Workflows - defined by humans, executed with AI
You connect multiple AI tasks in a predefined sequence you design: "Every morning, check my calendar, summarize meetings, and email me a brief." The AI executes each step you programmed, but if something unexpected happens, the workflow breaks because it can only follow the path you set.
Level 3: AI Agents - high-level mission with autonomous decisions
This is the fundamental shift. Instead of programming every step, you give the AI agent a high-level goal and access to tools, and it figures out how to achieve that goal autonomously.
You might say to your AI sales assistant agent: "Schedule coffee meetings with my top five prospects next week." The agent then:
Checks your calendar for availability
Identifies the prospects from your CRM
Finds mutually available times
Drafts personalized meeting invitations
Confirms the plan anmd details with you
Sends the invitations
Follows up if someone doesn't respond
The critical difference: you didn't tell it to do those steps in that order. The agent reasoned about the best approach, decided which tools to use, and adapted when it encountered obstacles. If the first attempt at finding time doesn't work, it tries different approaches - all autonomously.
Another example: you tell an agent "create social media posts based on today's industry news." The agent reasons: What's the most efficient approach? Should I manually copy articles? No, better to compile links and use tools to fetch content. Which tools? Google Sheets for organization, Perplexity for summarization, Claude for writing. Then it executes those steps, critiques its own output, and iterates until the posts meet quality standards - without you debugging each attempt.
What this means for business leaders
Gartner predicts that by the end of 2026, 40% of enterprise applications will be integrated with task-specific AI agents, up from less than 5% today. Gartner In a May 2025 PwC survey of 300 senior executives, 88% say their team or business function plans to increase AI-related budgets in the next 12 months due to agentic AI, and 79% say AI agents are already being adopted in their companies.
But here's the critical distinction that most miss: many of what vendors call "AI agents" today are really just Level 2 workflows with better marketing. The most common misconception is referring to AI assistants as agents, a misunderstanding known as "agentwashing".
The opportunity is real, but so are the implications. When AI systems can make decisions and take actions autonomously, governance becomes non-negotiable. You need to know which agents are operating, what they have access to, and how to revoke permissions if something goes wrong - exactly the kind of responsibility-at-scale thinking this newsletter issue explores.
If you're not sure where to begin, you're always welcome to reach out to me.
For a visual walkthrough of these three levels using practical examples, I recommend this explainer video. It demonstrates the progression from basic LLM interactions (Level 1), to predefined AI workflows (Level 2), to true agentic systems that reason and act autonomously (Level 3):