What the Heck Is an AI Agent?
The term 'Ai Agent' have been loosely thrown around. It may not be what you think.
In our last post, we explored how LLMs are finally learning to use real-world tools… thanks to something called MCP.
It was a big unlock.
For the first time, models like ChatGPT could do more than just answer questions. They could fetch live data, trigger workflows, run SQL queries, and more… all by plugging into external software through a shared communication layer.
But here’s the thing: tools alone aren’t enough.
Because knowing how to use a tool is different from knowing when to use it. Or why. Or what to do next if something goes wrong.
And that’s exactly where AI agents come in.
🤖 LLMs Are Smart… But Still Passive
Let’s say you type into ChatGPT:
“Find the cheapest flight to Bangalore next week and email me the best option.”
This sounds simple. But under the hood, it requires multiple steps: searching a travel site, extracting prices, comparing options, writing an email, and sending it to you.
Even if you gave the model access to tools like a flight API and your Gmail (via plugins or protocols like MCP) it still wouldn’t know how to put it all together on its own. You’d have to instruct it manually, step by step.
LLMs are powerful, but they’re inherently passive. They react to what you type. They don’t set goals. They don’t remember previous steps unless you force them to. And they definitely don’t reflect on whether they did a good job or not.
So while MCP gave LLMs a set of tools, they still lack something critical: initiative.
That’s the missing layer AI agents aim to solve.
💡 So What Is an AI Agent?
At its core, an AI agent is a language model that can think in loops instead of just lines. It can make decisions, take actions, check the results, and adjust its plan if needed… all without constant human prompting.
More specifically, an agent:
Plans what needs to be done
Chooses which tool or action to use
Executes the step
Reflects on whether the step worked
Then repeats or pivots based on what it learns
It’s not just answering a single prompt. It’s working toward a goal.
This type of decision-making is often called the planning pattern: where the agent breaks down a big goal into smaller, manageable steps. Think of it like how a product manager might outline a roadmap before jumping into tasks.
Still fuzzy? Try this:
You assign a task to a junior teammate: “Hey, can you check our latest signups, email me a summary, and flag anything odd?”
They wouldn’t ping you after every click. They’d break the task into steps, pick the right tools (CRM, email, maybe a spreadsheet), handle the hiccups, and report back when it’s done.
That’s the core idea behind agents. It’s about autonomy: the ability to reason, plan, and act using the tools available.
🧨 What AI Agents Are Not
Let’s clear something up.
With all the buzz around “AI agents,” it’s easy to assume we’ve built digital employees. We haven’t. Not yet.
Agents today aren’t magical. They’re not fully autonomous. And they’re definitely not general-purpose.
What we have right now are systems that try to behave autonomously… but often break in predictable ways. They can:
Loop endlessly on tasks
Forget what they just did
Misuse tools or make inefficient decisions
Get stuck without clear fallback logic
We’re not at the stage of Jarvis. We’re closer to an overenthusiastic intern who wants to help but still needs supervision, guardrails, and a bit of luck.
Still, even a half-reliable intern can save you a lot of time.
🚫 Not All Workflows Are Agentic
Most of what we call “AI” today isn’t agentic. It’s either scripted automation or single-shot response systems.
There are actually three levels of workflow you’ll see in the wild:
AI Automation: Fixed sequences. No thinking. Just follow steps
AI Workflows: The model responds intelligently, but only once (no planning, no adaptation)
Agentic Workflows: Dynamic. Multi-step. Self-correcting. These systems plan, act, reflect, and improve over time
Agentic workflows are what make AI feel strategic instead of reactive.
They’re not just executing. They’re thinking about how to execute… and adjusting when the situation changes.
That’s what gives agents their real-world value.
🔍 An Agent-equivalent that you’ve probably already used
Here’s where we need to be careful.
You’ve likely interacted with systems that feel agent-like… but technically, they’re not full agents. Not yet.
Think about:
A meeting assistant that checks calendars and proposes times
An email tool that drafts responses based on CRM data
GitHub Copilot suggesting your next line of code
ChatGPT with plugins fetching info from the web
AutoGPT-like demos that complete tasks over multiple steps
These tools automate and simplify tasks. Some even use LLMs under the hood. But they often follow a fixed flow, without real decision-making or feedback loops. They're not fully autonomous, but they're task helpers, not agents.
So why bring them up?
Because they show us what’s possible. These systems are the stepping stones. They’re training us (and the models) for what’s coming next.
🚀 Why Agents Matter (Even In Their Current State)
Despite their limitations, AI agents mark a turning point.
They shift our interaction with AI from command-response to goal-driven collaboration.
Instead of you saying “Do X,” you’ll soon say:
“I need this done. Figure out how.”
And the model will break down the task, choose tools, run steps, even handle errors, and finally report back!
Even basic agents can save time by running complex tasks, managing communication flows, or analyzing and acting on data across tools.
And here's the kicker: agentic workflows are already moving from prototype to production. In industries like customer support, data analysis, and internal ops, early agents are being deployed in real-world systems… not just research demos.
If you’re still thinking about AI as a chatbot, you’re a step behind.
The frontier is agents that reason, adapt, and execute.
📌 Why This Ties Back to MCP
Remember that post about MCP (Model Context Protocol)?
That gave LLMs the ability to interact with real-world tools: databases, APIs, files, and apps.
But MCP doesn’t tell the model when to use a tool. Or which one. Or how to sequence them.
That’s the agent’s job.
So in many ways, MCP is the foundation.
But agents are the structure that sits on top of it.
One gives you capabilities.
The other gives you direction.
🔮 What’s Next: Let’s Peek Under the Hood
So far, we’ve covered the “what” and “why.”
In the next post, we’ll explore the “how.”
What are agents made of?
How do they reason, plan, and reflect?
What tech powers them today: from planning loops to memory to toolkits like AutoGen, CrewAI, and LangGraph?
We’ll even look at where they still break… and what’s being done to fix that.
Because understanding how agents work (even if you’re not building them) helps you collaborate with them more intelligently.
🧠 Final Thought: From Tools to Teammates
If MCP gave our LLMs a toolbox…
AI agents are the ones picking up the tools and deciding what to build.
It’s early. It’s imperfect.
But it’s real.
And it’s where the future of AI is heading.
Next on LLMentary:
We’ll take apart the core components of modern AI agents… what they need to function, how they make decisions, and why the agent frameworks of today are laying the groundwork for the AI teammates of tomorrow.
Until then, stay curious.
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