Explaining AI Agents

Explaining AI Agents
Introduction
The term “AI agent” has been popping up everywhere lately, but underneath
the

marketing

side

of

things,

theoretical

programs

transformed

into

practical

applications.

2025

was

the

year

that

AI

went

from

just

talking

and

started

taking

action.

The

rise

of

new

protocols

have

bridged

the

gap

between

the

power

of

LLMs

and

external

software,

giving

AI

the

agency

to

perform

tasks

with

semi-autonomous

control.

However,

with

every

single

company

pushing

out

their

own

rendition

of

agentic

AI,

it

begs

the

question:

what

exactly

are

AI

agents?

What are AI Agents?
Agency is the ability to make decisions or act independently. This is the core
concept

that

drives

AI

agents.

Previously,

LLMs

were

limited

to

processing

and

answering

questions

with

the

information

they

had

access

to.

AI

agents

are

tools

that

are

powered

by

LLMs

with

the

ability

to

independently

access

external

APIs

and

software

to

plan,

reason,

and

execute

tasks.

How are they different from typical chatbots?
The two main differences between AI agents and chatbots is their capacity for
action

and

memory.

Though

both

are

powered

by

LLMs,

each

was

designed

for

a

different

purpose.

Chatbots

were

built

for

dialogue

and

simple

information

retrieval,

but

agents

were

made

to

handle

intricate

workflows

and

independent

operations.

What makes an AI agent different from the typical chatbot is its ability to
engage

with

software

beyond

the

chat

window.

Chatbots

are

limited

to

the

information

they

have

available

and

the

data

it

was

trained

on,

but

AI

agents

go

beyond

that

with

their

ability

to

access

other

tools.

Your

expected

response

with

a

chatbot

is

retrieval

of

information,

but

with

agents

you

can

expect

real-time

completion

of

tasks

with

the

right

tools.

The other defining aspect of AI agents is their long-term memory. Chatbots
are

typically

restricted

to

the

information

in

a

single

conversation

or

from

the

files

uploaded

in

it.

AI

agents

contain

a

persistent,

long-term

memory

that

allow

them

to

recall

and

learn

from

past

interactions

to

provide

personalized

responses

to

the

context

provided.

The

memory

capabilities

that

AI

agents

possess

will

make

them

feel

like

a

work

partner

that

knows

exactly

what

you

need,

rather

than

a

mere

information-grabbing

tool.

How does DeepTutor utilize AI Agents?
At DeepTutor, we have released our own rendition of an AI agent with a new
chatting

option

called

Agent

Mode.

In

Agent

Mode,

we

give

our

users

the

ability

to

chat

to

their

entire

Zotero

library

at

once.

This

means

that

users

do

not

have

to

upload

the

specific

documents

they

need

to

extract

information.

All

users

have

to

do

is

prompt

what

they

need

and

our

AI

agents

can

find

what

they

need

with

a

simple

keystroke.

A major benefit that our users gain is that they do not have to waste time
sorting

through

their

vast

amount

of

files

to

utilize

our

service.

Agent

Mode

supports

semantic

search,

meaning

that

users

can

ask

us

what

type

of

documents

we

are

looking

for

and

we

find

it

for

them.

This

means

conducting

deep

research

across

thousands

of

files

and

quickly

identifying

similar

methods

and

insights

across

papers

with

no

time

added.

We

give

our

users

the

time

they

need

to

focus

and

synthesize

their

findings,

with

no

extra

strain

on

their

energy.