What Does an AI Agent Actually Do?
You've heard the term a thousand times. "AI agent this," "agentic AI that." Every vendor with a chatbot slaps "agent" on their product page. I run a product engineering company called SIVARO — we build data infrastructure and production AI systems. My team ships these things. And I'll tell you straight: most people talking about AI agents have never built one that works in production.
So let's cut through the noise. What does an AI agent do exactly?
An AI agent is a software system that perceives its environment, makes decisions, and takes actions to achieve a goal — without a human hand-holding it through every step. Think of it as the difference between a calculator and a chess engine. The calculator does exactly what you tell it to. The chess engine decides which move to make, based on its own analysis of the board.
That's the core. But the details matter. Hard.
In this guide, I'll walk you through what agents actually do under the hood, how they differ from chatbots, when they break, and why most people get this wrong. I'll reference real systems we've built at SIVARO, mistakes we've made, and patterns that actually survive contact with customers.
The Core Loop: Perceive, Decide, Act
Every AI agent, from the simplest to the wildest multi-agent swarm, runs on three things:
- Perception — getting data from the world
- Decision — figuring out what to do next
- Action — doing something that changes the world
That's it. The complexity comes from how you implement each step.
Let me give you a concrete example. At SIVARO, we built a customer support agent for a mid-size e-commerce platform. The perception was incoming support tickets — text, order IDs, customer history. The decision engine was an LLM with a structured prompt and a set of tools (check order status, process refund, escalate to human). The action was either resolving the ticket or routing it.
The agent didn't need a person to say "now check the shipping status." It just did it. That's the whole point.
AI Agents, Clearly Explained breaks this loop down better than most academic papers. Watch it if you want a visual walkthrough.
Chatbots vs Agents: The Line That Keeps Moving
Most people think: "Is ChatGPT an AI agent?" Answer depends on who you ask.
I'll give you my take. ChatGPT in its default form is not an agent. It's a chatbot. You type, it responds. No perception beyond your message. No actions beyond generating text. No persistent goal.
But here's where it gets interesting. OpenAI's ChatGPT agent mode (introduced in early 2025) does act like an agent — it can browse the web, run code, and use tools autonomously. The ChatGPT agent documentation shows it completing multi-step tasks like "find me a flight and book it" without asking for permission at each step.
So is ChatGPT an AI agent? Not really. Not consistently. Not reliably. One analysis points out that ChatGPT lacks persistent memory of goals across sessions and doesn't proactively pursue objectives. That's the difference.
A chatbot responds. An agent pursues.
The Action Layer: Where Agents Earn Their Keep
Here's what separates real agents from glorified chatbots: tools.
An agent needs to do things in the world. Not just generate text. Send an email. Update a database. Trigger a deployment. Book a meeting.
This is where most "agent" products fall apart. They can talk a good game, but when you ask them to actually do something — write to a production database — they panic.
At SIVARO, we use a pattern called "tool-grounded actions." Every action the agent can take is a registered function with typed inputs, outputs, and error handling. The LLM generates a structured call, and we execute it in a sandboxed runtime.
python
# Example tool definition for an AI agent
@tool("check_order_status", "Look up current status of any order by ID")
def check_order_status(order_id: str) -> dict:
"""Returns order status, expected delivery date, and carrier info."""
# This hits our internal order management system
result = db.query("SELECT status, delivery_date, carrier FROM orders WHERE id = ?", order_id)
if not result:
return {"error": "Order not found"}
return {
"status": result["status"],
"expected_delivery": result["delivery_date"].isoformat(),
"carrier": result["carrier"]
}
The agent reasons about what tool to call, with what arguments, and then we execute it. No magic. Just structured function calls wrapped in an LLM's reasoning loop.
The Decision Problem: Planning vs Reacting
Not all agents plan ahead. Some react to each observation as it comes. Some build multi-step plans. Some do both.
Think of it like driving. A reactive agent is like a driver who only looks 50 feet ahead — works fine at low speeds. A planning agent builds a route for the whole trip.
I used to think planning was always better. Then we built a supply chain agent that tried to plan 48 hours ahead. It made beautiful plans. And then the real world happened — a shipment got delayed, a price changed, a warehouse went offline. The agent replanned from scratch every time. It was like watching Sisyphus debug a spreadsheet.
What Are AI Agents? | IBM describes this tension well: "Reactive agents are simpler and more robust. Deliberative agents are more powerful but brittle."
Our solution? Hybrid. Short-term reactive, medium-term planned. The agent builds a rough plan for the next four hours (planning), then reactively adjusts based on real-time data (reactive). We call it "plan-execute-adjust." Works better than either pure approach.
A Quick Note on the 30%% Rule
You'll hear people throw around "the 30%% rule for AI." There's no formal academic definition — it's a practitioner heuristic. Roughly: If your agent works 100%% of the time in a demo, it'll work about 30%% of the time in production.
I've seen this pattern at least a dozen times. The demo environment has perfect data. Clean inputs. Friendly latency. The production environment has mistyped addresses, missing fields, timeout errors, and users who ask "what if" questions that weren't in the prompt.
The 30%% rule isn't a law. It's a warning. Expect your agent to fail in ways you didn't anticipate. Build guardrails. Log everything. And don't ship an agent that can write to production without human approval — not until you've seen six months of production data.
Memory: The Thing Everyone Forgets
An agent without memory is a goldfish. It remembers nothing between interactions. You ask it to "find a hotel for next Tuesday" and then "actually, make that Wednesday" — and it books you a hotel for Tuesday because it forgot the correction.
The AI Engineer substack makes a good point: "Memory is what separates a task executor from an autonomous agent."
There are three types of memory in agent systems:
- Conversation history — what was said in this session
- Working memory — the current goal and progress
- Long-term memory — preferences, patterns, learned behaviors from past sessions
Most production agents handle #1. Some handle #2. Almost nobody handles #3 well.
At SIVARO, we store long-term memory as structured facts in a vector database. The agent writes facts like "user prefers window seats" and retrieves them when relevant. It's not perfect — retrieval quality is still a research problem — but it's better than starting from scratch every time.
python
# Simplified memory write pattern
def remember_fact(agent_state: dict, fact: str, scope: str = "user"):
"""Store a fact in the agent's long-term memory."""
embedding = embedding_model.embed(fact)
vector_db.insert({
"fact": fact,
"embedding": embedding,
"user_id": agent_state["user_id"],
"scope": scope,
"timestamp": datetime.now()
})
Multi-Agent Systems: The Hype vs Reality
You can't escape the "multi-agent" trend. Everyone wants to build a team of specialized agents that talk to each other. I've seen architecture diagrams with 12 agents all coordinating. They look like a nervous system drawn by a caffeinated architect.
Here's the truth: multi-agent systems are powerful but fragile.
We built one at SIVARO — a content moderation pipeline with three agents: one for text analysis, one for image analysis, one for escalation to humans. Each agent specialized. The text agent flagged problematic language. The image agent flagged visual violations. The escalation agent decided if a human needed to review.
It worked. Until it didn't. The agents started arguing in their communication channel — one flagged "violence in text" about a boxing match article, the other said "no visual violation," and the escalation agent got confused and escalated everything.
The fix? We reduced the communication to strict structured data instead of natural language. No "I'm not sure about this image." Just {"flag": True, "confidence": 0.87, "category": "gore"}.
Agentic AI, explained from MIT notes: "The biggest challenge in multi-agent systems is coordination — getting agents to agree on what they're doing without conflicting."
Keep it simple. Two or three agents. Structured communication. Test the hell out of it.
When Agents Break: Failure Modes I've Seen
I've been building these systems since 2018. I've seen agents fail in spectacular ways. Here are the most common:
Hallucinated tool calls. The agent calls send_email(to="customer@example.com", body="...") — but the body contains a made-up order number that doesn't exist. The customer gets confused. You get a support ticket about your support ticket.
Infinite loops. The agent tries to check inventory, finds none, decides to restock, places an order, checks inventory again, sees the same item, orders again. We caught this after 47 duplicate orders in staging.
Goal drift. The agent's task was "find flight prices under $500." After 30 minutes, it was "find flights to anywhere under $1000." The goal decayed because the prompt didn't reinforce it.
Cost explosions. Each agent call costs money. An agent that loops 50 times costs 50x what you planned. We had a customer's bill hit $4,000 in one night because an agent got stuck in a retry loop.
Google Cloud's explanation of AI agents mentions: "Agents must have clear boundaries on autonomy — both in scope and budget."
Build in cost limits early. We use a max_tokens_per_session and max_tool_calls_per_session counter. Hard stop when hit.
Code Example: A Minimal Agent Loop
Here's the simplest agent loop that actually works in production. No frameworks. No orchestration layers. Just the core pattern.
python
import json
from openai import OpenAI
client = OpenAI()
tools = [
{
"name": "get_weather",
"description": "Get current temperature for a city",
"parameters": {
"type": "object",
"properties": {
"city": {"type": "string"}
},
"required": ["city"]
}
},
{
"name": "book_flight",
"description": "Book a flight between two cities on a specific date",
"parameters": {
"type": "object",
"properties": {
"from": {"type": "string"},
"to": {"type": "string"},
"date": {"type": "string"}
},
"required": ["from", "to", "date"]
}
}
]
def agent_loop(user_input: str):
messages = [{"role": "user", "content": user_input}]
max_steps = 10
step = 0
while step < max_steps:
response = client.chat.completions.create(
model="gpt-4o",
messages=messages,
tools=tools
)
msg = response.choices[0].message
if not msg.tool_calls:
# Agent decided to respond directly
return msg.content
for tool_call in msg.tool_calls:
# Execute the tool
function_name = tool_call.function.name
arguments = json.loads(tool_call.function.arguments)
if function_name == "get_weather":
result = {"temperature": 72, "unit": "F"}
elif function_name == "book_flight":
result = {"status": "confirmed", "booking_id": "ABC123"}
else:
result = {"error": "Unknown tool"}
messages.append({
"role": "tool",
"tool_call_id": tool_call.id,
"content": json.dumps(result)
})
step += 1
return "Maximum steps reached. Escalating to human."
This isn't production-grade — missing error handling, memory, budget limits — but it's the skeleton. Every agent I've shipped looks like this under the hood.
The 30%% Rule Revisited: You Need Guardrails
I mentioned the 30%% rule earlier. Let me make it concrete.
We deployed an agent for automated customer refunds. In the demo, it handled 50 test cases perfectly. In production, here's what happened in the first week:
- 3 times it processed refunds for orders that were already refunded
- 2 times it offered refunds above the customer's actual payment amount
- 1 time it approved a refund for a product that didn't belong to that customer
The 30%% rule hit hard. Our guardrails weren't tight enough.
We added three things:
- Human-in-the-loop for any action above $50
- Validation layer that checked refund amounts against actual order values
- Logging every action to a separate audit store
After that, the agent handled 92%% of cases without incident. The 30%% rule was our wake-up call.
Building an Agent? Start With the Data Layer
At SIVARO, we're a data infrastructure company. So I'm biased. But I've seen more agents fail because of bad data than bad AI.
Your agent needs reliable access to:
- Current state — what's true right now
- Historical context — what happened before
- User identity — who is this person and what can they do
If any of these are flaky, your agent will make bad decisions. It doesn't matter how good your LLM is. Garbage in, garbage out — that hasn't changed.
AWS's AI agent documentation puts it well: "The quality of an agent's actions is directly limited by the quality of its data sources."
We spend 60%% of our agent development time on data pipelines and API integrations. The model work is the easy part. The boring infrastructure is what makes it work.
FAQ: What Does an AI Agent Do Exactly?
Is ChatGPT an AI agent?
Not in the strict sense. ChatGPT can perform agent-like tasks when using tools (browsing, code execution), but it lacks persistent goals, long-term memory across sessions, and autonomous pursuit of objectives. It's a powerful chatbot with agent features, not a true agent.
What does an AI agent do exactly in simple terms?
It perceives input, decides what action to take, and executes that action — all without a human telling it each step. Like a smart thermostat that senses temperature, decides to turn on heat, and does it.
What's the 30%% rule for AI?
A heuristic: if your agent demo works 100%% of the time, expect it to work roughly 30%% of the time in production. Demos are clean. Production is messy. Build guardrails accordingly.
Can I build an AI agent without coding?
Sort of. Platforms like the ChatGPT agent interface let you configure agents visually. But real control requires code. The less coding you do, the less control you have.
How many tools should an agent have?
Fewer than you think. We limit production agents to 5-8 tools max. More tools = more decision surface = more failure modes. Add tools slowly.
Do AI agents remember past conversations?
Only if you build memory into them. Most agents are stateless — they forget everything after the session ends. Persistent memory requires storing and retrieving facts, which is a separate engineering effort.
What's the biggest mistake people make with AI agents?
Over-automating too fast. They give the agent write access to production systems before understanding failure modes. Start read-only. Add write access slowly. Always have a kill switch.
Are AI agents ready for enterprise use?
Yes, but with limits. At SIVARO, we have agents handling customer support, data enrichment, and content moderation in production. But each one has human oversight, cost controls, and clear boundaries. Unsupervised agents are still research projects.
Where Agents Are Going Next
The next 12 months will bring three shifts:
-
Better memory systems. Vector databases and structured memory will get cheaper and faster. Agents will remember you across weeks, not just minutes.
-
Tool ecosystems. More services will expose agent-friendly APIs. Right now, hooking an agent to Salesforce takes weeks. In 2026, it'll take hours.
-
Regulation. The EU AI Act and similar frameworks will require agents to log decisions and explain actions. If you're building an agent today, build logging. You'll need it.
The Reddit discussion on ChatGPT as an agent shows people are already confused about what's an agent vs what's a chatbot. That confusion will get worse before it gets better. The companies that build clear, honest agent systems — that say "this can do X but not Y" — will win.
Final Thought
I've been building production AI systems since 2018. SIVARO has shipped agents that process 200,000 events per second. I've seen agents make brilliant decisions. I've seen them make catastrophic ones.
The question "what does an AI agent do exactly?" has a simple answer — perceive, decide, act — and a complex one — plan, reason, remember, learn, coordinate, fail, retry.
Build the simple version first. Test it ruthlessly. Add complexity slowly.
And never trust a demo.
Nishaant Dixit — Founder of SIVARO. Building data infrastructure and production AI systems since 2018. Built systems processing 200K events/sec.