How to Build an AI Agent for Lead Qualification with MCP

MCP in simple terms

MCP (Model Context Protocol) is a practical way to let an AI Agent connect to tools through dedicated “MCP servers.”

Instead of an agent saying, “You should send an email,” it can actually send it through a Gmail MCP server. Instead of saying, “Store this lead somewhere,” it can write to Google Sheets.

That is how AI Agents move from text generation to real workflows.

How to build an AI agent: my high level approach

I will keep this intentionally high level because the exact implementation depends on your stack, but the thinking stays the same. If you want the detailed build, jump to the video section in the middle of this article.

Here is the approach I use when we design AI Agents with customers.

1) Start from one workflow, not a generic assistant

If your goal is “build an AI Agent for everything,” the result is usually weak.

Pick one workflow with:

• clear inputs

• clear actions

• clear success criteria

Examples: qualify a lead, process a claim, answer policy questions with citations, triage IT requests.

2) Define decisions and guardrails before tools

This is the part people skip, then they lose trust.

Decide things like:

• when the agent is allowed to act

• when it must ask a human

• which data it can store and where

• what it must never say or promise

3) Connect the minimum tools required

Do not connect ten systems on day one.

Connect the two or three tools that unlock the workflow. MCP makes this cleaner because each tool has a clear server and a predictable interface.

4) Make outputs auditable

If the agent sends an email, log it.
If it classifies a document, store the label and the reason.
If it rejects something, store the decision trail.

This is a big part of trustworthiness.

5) Measure and iterate with real traffic

The best design happens after you see real user inputs.

Track:

• completion rate

• error types

• time saved

• quality signals (customer satisfaction, fewer reopens, faster response times)

If you are serious about how to build agentic ai, this loop matters more than any prompt.

Example: an AI Agent that qualifies leads and sends follow up emails

One of my favorite starter projects is lead qualification because it is simple, measurable, and immediately valuable.

The idea:

• the AI Agent collects lead information conversationally

• it scores the lead based on the answers

• it writes the lead to Google Sheets

• it sends a follow up email to the hottest leads via Gmail

This agent uses two MCP servers:

• Google Sheets MCP server

• Gmail MCP server

And it uses prompt driven actions to orchestrate the flow.

Video walkthrough

If you want the exact step by step build, we published a full tutorial that shows how the agent works and how the MCP connections are used.

After you watch it, come back to the next sections. This is where you will get more ideas for what to build next.

The template for this AI Agent is also available in our Community, so you can import it into your project in just a few clicks.

Import the AI Agent template into your project with just 3 clicks.

Agentic AI use cases by industry

When teams ask “how to build an ai agent,” what they often need is not another definition. They need examples that map to real operations.

Below are use cases we frequently discuss with customers. I am keeping them concrete, with a simple input, decision, and action pattern.

Insurance industry

Claims Processing Agent

The user enters policy data, LLMs match and validate it, and the AI Agent approves or rejects the claim with a personalized email.

Underwriting Copilot

The user provides details, LLMs validate against policy terms, and the AI Agent sends a summary.

Policy Q&A Agent

The user enters a question. The AI Agent retrieves the most relevant text, the LLM generates a tailored response, and clear references are displayed.

Why these work well: insurance teams already have structured rules, lots of documents, and repetitive decisions. An AI Agent can reduce cycle time while keeping auditability.

Government and public sector

IT Support Agent

The user submits an IT question, LLMs search internal docs, generate an answer, or escalate to a human if needed.

Compliance Agent

The user uploads files and a recipient email, the agent pulls the relevant regulations, the LLM analyzes, and a report is emailed.

Document Intake Agent

The AI Agent quickly scans documents, extracts key data, and classifies them into the right category and system.

Why these work well: public sector workflows often have high volumes, clear procedures, and document heavy processes. Agentic AI helps with triage, routing, and compliance checks without forcing teams to change everything overnight.

Private lending

Application Risk Agent

The user uploads documents, the LLM checks for issues, the system classifies risk, and an email or Drive log is created.

Background Report Agent

The AI Agent retrieves and validates background reports via API or uploaded docs for faster due diligence.

Closing Compliance Agent

The user uploads files, the LLM flags missing or non compliant items, and the system emails issues to the recipient.

Why these work well: lending has strict checklists, time pressure, and heavy documentation. AI Agents can speed up validation and reduce manual back and forth, as long as the process is controlled and logged.

Final takeaway

If you are learning how to build an AI agent, start small but make it real:

• choose one workflow

• connect the minimum tools

• add guardrails and logging

• measure results with real usage

That is how you build AI Agents that deliver outcomes, and that is what Agentic AI should mean in practice.

See it in action