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What Is an AI Agent, Actually?
AI agents are everywhere in conversation but poorly understood. A clear, jargon-free explanation of what they are, what they aren't, and why it matters.
What Is an AI Agent, Actually?
IN 30 SECONDS
"AI agent" has become one of the most used and least understood terms in technology. People nod along in meetings, vendors use it for everything from chatbots to autonomous systems. Here's what it actually means, without the jargon or hype.
The real starting point
Let's acknowledge something: a lot of people are confused about AI agents but don't want to admit it. The terminology is genuinely confusing. The same words mean different things in different contexts. And the space is moving so fast that what was true six months ago may not be true now.
This isn't a knowledge failure. It's a communication failure by the industry. So let's fix it.
The simple definition
An AI agent is an AI system that can take actions, not just generate text.
A regular chatbot like ChatGPT in a browser generates responses. You ask a question, it answers. Useful, but fundamentally passive.
An AI agent goes further. It can:
- Use tools: Search the web, execute code, call APIs, interact with software
- Plan and reason: Break tasks into steps, decide what to do next based on results
- Maintain state: Remember what's happened, track progress toward goals
- Work toward objectives: Not just responding to prompts, but pursuing outcomes
The key difference: a chatbot tells you how to book a flight. An agent actually books it.
The distinction that matters
There's a critical difference between "AI agent" and "agentic AI" that trips people up:
AI Agent
A specific system that executes tasks. Rule-following, often single-function. Example: a booking assistant that handles reservations.
Agentic AI
Systems with high autonomy - reasoning, planning, adapting, coordinating. Example: a research system that decides what to search, when to stop, how to synthesise.
All agentic systems are agents. Not all agents are agentic.
Why this matters: Most "AI agents" in production today are actually agentic workflows - AI systems with tool access and orchestration, but operating with human oversight. Fully autonomous agents are still rare, and for good reason.
The terminology trap
Here's where confusion multiplies: the same words mean different things to different people.
"Skills" - Anthropic uses this to mean filesystem-based expertise packages (SKILL.md files) that load automatically. Vercel used it to mean on-demand retrieval systems. General conversation uses it to mean any capability. Three completely different things, same word.
"Tools" - To engineers, these are function calls and MCP servers. To business users, they're integrations. To marketers, they're features.
"Memory" - Engineers mean context persistence mechanisms. Users mean "it remembers me." These are not the same thing.
PRACTICAL ADVICE
When someone uses any of these terms, ask what they mean specifically. The same word often refers to different things in the same meeting. This isn't pedantic - it's essential for making good decisions.
What most enterprises actually need
Here's something the hype obscures: most organisations in 2026 should be building agentic workflows rather than autonomous agents.
The practical path
- 1Agentic workflows: Predefined orchestration where code controls the sequence. Predictable, governable, reliable.
- 2Human oversight: Agents suggest, humans approve. Or agents work while humans monitor.
- 3Clear boundaries: Know what the AI can decide alone vs. what requires human judgment.
- 4Governance first: Security, compliance, and accountability designed in, not bolted on.
The fully autonomous agent - one that operates independently with minimal oversight - is coming, but it's not where most organisations should start. Get the foundations right first.
The enterprise reality check
The signals we're seeing:
- Most enterprises remain cautious about fully autonomous agents - trust is still being established
- Governance and cost concerns are leading some organisations to pause or scale back agent projects
- The organisations making progress are typically starting with workflows, not fully autonomous agents
This isn't pessimism. It's realistic planning. AI agents represent a genuine capability shift. But understanding what they actually are matters more than adopting the latest terminology.
The questions to ask
When someone proposes an AI agent solution, ask:
Clarifying questions
- Is this a workflow (predefined steps) or an autonomous agent (model decides)?
- What's the human oversight model?
- What happens when it gets something wrong?
- How do we measure success?
- What can it do alone vs. what needs human approval?
These questions aren't skepticism. They're the foundation for AI that actually works.
The bottom line
AI agents are real, powerful, and getting better rapidly. They extend AI capability far beyond chat interfaces. Organisations that build this capability well will have significant advantages.
But the terminology is a mess. The hype often exceeds the reality. And the path forward is usually more measured than the headlines suggest.
Start with clarity about what you're actually building. Get the governance right. Build workflows before autonomous agents. And don't pretend to understand terms that genuinely are confusing.
The organisations that succeed with AI agents will be those that combine genuine capability with clear-eyed assessment of where they are and what they need.
FAQs
What is an AI agent in simple terms?
An AI system that can take actions, not just generate text. It uses tools, plans multi-step tasks, and works toward goals. A chatbot answers questions; an agent does things.
What does 'agentic AI' mean?
AI systems with high autonomy - reasoning, planning, adapting, and coordinating with minimal human guidance. It's a step beyond simple task-execution agents.
Are AI agents the same as chatbots?
No. Chatbots respond to prompts. Agents use tools, maintain state, and execute work. The difference is generating answers vs. actually doing things.