AI Agents explained

The Truth About AI Agents (in 60 Seconds)

AI Agents Explained. Image by Rajashree Rajadhyax

Everyone’s using chat companions like Chat GPT, Perplexity, Claude and the like. You’ll agree that we all find them useful in many ways. Just as we were getting a grip on generative AI, the term AI agents started gaining popularity. 

Everybody’s talking  about "AI agents" now, but most of the time, it's either overhyped, vague, or poorly defined. Every week, there’s a post claiming, “We built an AI agent that will change your life.” But peel back the marketing gloss, and you’ll find a wide range of systems,  from simple scripts that run a series of prompts, to complex multi-step, self-directed workflows, all being labelled “agents.” 

The truth is: AI agents aren’t a single thing with a rigid definition. They can have different levels of independence, and once you see that range, all the buzz feels clearer.

The Core Idea of an AI Agent

At its heart, a software can be called an ‘AI agent’ if it can:

1. Perceive an environment or situation (through inputs like text, speech, images, or structured data).

2. Decide what to do next based on that input, its goal, and possibly some memory of past interactions.

3. Act on those decisions, which could mean generating a response, calling tools, manipulating data, or even controlling physical devices.

The “agent” part comes from autonomy. An AI agent is designed to carry out tasks with minimal step-by-step human instruction. Instead of telling it exactly what to do, you tell it what you want done, and it figures out the steps.

Why the Spectrum Matters

1. Prompt Chaining

Some “agents” are really just a series of prompts chained together. They have no persistent memory, no decision branching, and no ability to adapt if something unexpected happens. They’re useful, but they’re basically pre-scripted workflows.

Example: A customer service “agent” that runs through a fixed series of LLM prompts to handle FAQs.

2. Tool-Using Agents

These agents can call external tools like databases, APIs, search engines, based on the context. They decide when to use which tool, not just follow a set script. This makes them far more adaptable.

Example: A research assistant agent that decides whether to search the web, summarize a document, or draft an email based on your query.

3. Goal-Directed Agents with Planning

Now we’re getting into more “agentic” behavior. These systems break a big goal into smaller sub-tasks, execute them in sequence, and adjust if something doesn’t go as expected. They can handle ambiguity better and don’t need constant supervision.

Example: An event-planning agent that, given “Organize a webinar on AI ethics,” can find speakers, schedule dates, and prepare promotional material.

4. Persistent, Multi-Agent Systems (The Advanced End)

At the far end of the spectrum are networks of agents that can work together, maintain long-term memory, learn from experience, and adapt strategies over time. 

Example: A set of specialized agents, one for legal research, one for financial modeling, one for negotiation, all working collaboratively to draft and review a business contract.

How important is autonomy?

The idea of completely autonomous agents sounds exciting. The thought of letting them run processes end-to-end without human input. But before we go in that direction, we need to ask: Do we really want that much autonomy in every situation?

Full independence isn’t always the goal

For many business processes, the sweet spot isn’t full independence, it’s AI taking over repetitive, well-structured tasks, while humans make the important calls. The right level of autonomy depends on the task: automating routine data entry is low risk, but deciding whether to halt production requires human oversight.

Usefulness is not all-or-nothing

​​Just because an AI agent isn’t advanced enough to handle an entire process on its own doesn’t make it less useful. Some might dismiss a simple prompt chain as “not a real agent”; but that’s missing the point. The focus should be less on labels and more on whether the solution works for our needs. Even partial automation can save time, reduce errors, and free people for more valuable work. Autonomy is a spectrum, and every step forward can bring meaningful impact.

The case for human-in-the-loop

Today’s AI isn’t flawless. It can make confident mistakes, miss subtle cues, or struggle when the environment changes suddenly. That’s why, in most real-world settings, the goal isn’t replacing human decision-making, it’s about collaboration, where AI handles the routine, and humans remain in control of critical decisions.

The Current Reality Check

Most of the “AI agents” you see today fall somewhere between tool-using agents and goal-directed agents with planning. They can be extremely useful; automating repetitive tasks, reducing cognitive load, and acting as skilled assistants, but they’re far from the autonomous, sentient beings that sci-fi imagines.

Why This Matters for AI Enthusiasts

When you hear “AI agent,” it’s easy to picture a fully autonomous digital assistant running your life. In reality, most agents sit somewhere on an autonomy spectrum; from simple task helpers to more adaptive systems.

For users, the label doesn’t matter, what matters is the utility:

• Does it help me get things done faster or better?

• Can I trust its output, or will I need to babysit it?

• Can I step in and guide it when things go off track?

• Does it fit easily into the tools I already use?

• Is the setup quick enough to be worth it?

And here’s the point: it’s completely ok if an agent isn’t fully autonomous. If it saves you time, reduces mistakes, or makes your workflow smoother, then it’s already valuable. Autonomy is a spectrum, and even small steps forward can bring real benefits.

Final Thought

AI agents aren’t something coming in the future, they’re already here and getting better all the time. But they’re not one single, clear thing. They can be as simple as a small prompt-based workflow or as advanced as a complex system of many agents working together.

Thinking of them as a spectrum helps us avoid all-or-nothing thinking. Instead of asking, “Is this a real AI agent?” we can ask, “Where does this fit on the spectrum and is that good enough for what I need?”

Because, in the end, the best agent isn’t the one with the flashiest marketing, it’s the one that actually works for you.