What Are Intents in AI Agents? The Backbone of Smart Automation | L17 AI

What Are Intents? The Backbone of AI Agents Explained Simply

How AI agents understand what people want — and why this single concept determines whether an agent works well or falls apart.

Introduction: Intents Are the Core of Every AI Agent

To most people, AI agents feel “intelligent” because they respond naturally.

But the real intelligence underneath is much simpler and more structured:

An AI agent works by identifying what the user is trying to do. That “what” is called the intent.

If the agent gets the intent right, the rest of the workflow flows smoothly.

If it gets the intent wrong, the agent:

misunderstands the question

sends the wrong reply

routes incorrectly

or gets stuck

This is why intents are the backbone of every high-quality agent.

In this article, we’ll break down:

What intents are

Why they matter

How they work

The difference between intents, entities, and routing

Real examples for developers, CRE operators, investors, and service businesses

How L17 AI designs intents so agents perform reliably at scale

By the end, you’ll understand how agents actually think — and how to design them effectively.

1. What Is an Intent? (The Simple Definition)

An intent is the goal behind a user’s message.

It answers this fundamental question:

What is the person trying to accomplish?

Examples:

“I want to book a tour.”

“I need pricing.”

“Why hasn’t maintenance shown up?”

“Where do I send my investment docs?”

“Do you have availability next week?”

“I want to speak to someone.”

These all represent different intents, even if the language varies.

This is why intent recognition is the #1 most important skill of an AI agent.

2. Why Intents Matter So Much

If a human assistant misunderstands your question, you correct them.

If an AI agent misunderstands your question, it derails the conversation.

Correct intents → Correct actions

Wrong intents → Wrong actions

Every workflow depends on this moment.

Imagine an investor asks:

“Can I get the updated pro forma?”

If the agent thinks the intent is “learn about the property,”

it might send a generic summary instead of the document.

Wrong intent = wrong action.

3. How Intents Work Inside an AI Agent

Every message a user sends goes through a process:

Step 1 — Detect the intent

The agent identifies which category the request belongs to.

Step 2 — Match it to a workflow

Each intent maps to a workflow, script, or action path.

Step 3 — Execute the correct action

This may include:

answering

gathering data

booking

checking availability

escalating

routing to a human

Step 4 — Keep context (memory)

The agent stores what was said to avoid repeating itself.

Step 5 — Continue the conversation

The right intent allows the agent to follow the right path.

4. Examples of Common Intents Across Industries

To make this more concrete, here are real examples.

A. Real Estate Developers — Top 10 Intents

Book a tour

Request pricing

Get availability

Ask about project timeline

Request brochure or plans

Speak to a sales rep

Investor inquiry

Ask for directions

Submit documents

General FAQs

B. CRE Operators — Top 10 Intents

Lease inquiries

Space availability

NNN/tax details

Qualifying a business

Schedule a showing

Request CAM breakdown

Report an issue

Vendor coordination

Tenant questions

Broker info request

C. Investor Relations — Top 10 Intents

Request latest deck

Ask about minimums

Distribution schedule

Update on project timeline

Request next GP call slot

Document questions

Wire instructions

Legal/LLC inquiries

Capital commitments

Portal access help

D. Hospitality / STR — Top 10 Intents

Book a room

Rates & pricing

Amenities

Check-in/out times

Troubleshooting issues

Directions

Request late checkout

Change reservation

Guest messaging

House rules

E. Service Businesses — Top 10 Intents

Request quote

Book appointment

Ask about availability

Billing questions

Cancellation

Troubleshooting

Speak to support

Learn about services

Reschedule

FAQs

5. Why Bad Intents Create Bad AI Agents

Here are the biggest problems caused by poorly defined intents.

Problem #1 — Overlapping Intents

Example:

“Book a tour”

“Schedule a showing”

If the agent thinks these are different intents, it may send different workflows or ask unnecessary questions.

Problem #2 — Too Many Intents

More intents ≠ better agent.

If you create 100 intents, you create:

confusion

complexity

inconsistent routing

misfires

Great AI agents typically use 12–25 core intents.

Problem #3 — No Intent Hierarchy

Some intents must override others.

Example:

“Speak to a human”

“Emergency”

“Cancel something”

These override everything else.

Problem #4 — Weak “Fallback” Intent

Fallback =

“What does the agent do when it doesn’t know?”

If your fallback is bad, the agent:

loops

gets lost

frustrates users

stops progressing

Fallback is a design skill.

6. Intents vs Entities vs Routing (Critical Distinctions)

People confuse these three concepts, but each one is different.

A. Intent = the goal

“I want to book a tour.”

Intent = tour booking

B. Entity = the variable inside the request

“I want to book a tour this Friday at 3pm.”

Intent = tour booking

Entities =

day = Friday

time = 3pm

Entities tell you specific details, not the goal itself.

C. Routing = where the issue goes

After detecting the intent, the agent decides:

Should it answer independently?

Should it gather info?

Should it escalate?

Should it schedule something?

Should it pass to a human?

Should it trigger automation?

Routing is the logic tree after the intent.

7. Real Examples of Intents, Entities & Routing (Side-by-Side)

Let's walk through a message:

User says:

“Can I take a tour of the new units on Saturday morning?”

Breakdown:

Intent = Book a tour

Entities =

unit type = new units

day = Saturday

time = morning

Routing =

Check calendar

Offer available times

Confirm booking

Send confirmation

Log into CRM

Another example:

“Can I get the updated investor deck?”

Intent = Request investor deck

Entities = None (straightforward)

Routing =

Provide secure link

Verify identity if needed

Log the request

One more:

“Maintenance never showed, what do I do?”

Intent = Maintenance issue

Entities =

missing appointment

Routing =

Create ticket

Escalate

Notify maintenance lead

Follow-up scheduled

8. How L17 AI Designs High-Performance Intents

We don’t just list intents.

We follow a systematic process:

1. Discovery — Identify Your Real Patterns

We analyze:

inbound messages

call transcripts

tenant / guest requests

investor FAQs

past email trails

staff pain points

your top 5–8 “operational gaps”

From this, we extract the core intents.

2. Consolidation — Merge Similar Intents

Example:

“Book a tour”

“Schedule a showing”

“I want to see the property”

“Can I come visit?”

“Are tours available?”

These all collapse into:

Intent: Book a tour

3. Prioritization — Create Intent Hierarchy

We rank:

emergency intents

high-value intents

sales intents

support intents

routing intents

This ensures the agent always picks the right path.

4. Mapping — Assign Actions to Each Intent

Examples:

Book a tour → check availability → schedule → confirm

Pricing → provide price sheet → collect needed info

Investor inquiry → send deck → book call → tag as LP

Maintenance → create ticket → escalate

This is where quality is built.

5. Testing — Break the System on Purpose

We test:

weird questions

slang

typos

emojis

long paragraphs

edge-case requests

If an intent breaks, we refine it.

9. Why Intents Are the Secret to Complex Multi-Agent Systems

When you have:

voice agent

chat agent

follow-up agent

workflow agent

…they all depend on intents to coordinate.

Example:

Voice agent → detects “book a tour” → logs lead → chat agent follows up → workflow agent creates tasks.

Intent-driven coordination = operational AI.

This is how we move from “cute bot” → business automation.

10. Final Take: If You Understand Intents, You Understand How AI Actually Works

Intents are:

the brainstem

the compass

the decision-driver

the foundation

of every high-quality AI agent.

If you get intents right, your agent feels:

smart

helpful

natural

consistent

reliable

If you get intents wrong, your agent feels:

broken

confused

repetitive

robotic

frustrating

Understanding intents helps you understand:

how agents think

how instructions turn into actions

how workflows execute

how multi-agent systems coordinate

how operations become automated

This is the real engine behind L17 AI — the quiet, invisible system that makes everything else possible.