Building AI Agents powered by MadKudu

🤖 What is an AI Agent?

An AI agent is an automation with AI in it.

In practice, this means there is one or more parts of it where an LLM makes a decision on its own instead of having only determinist "IF this THEN that" as rules.

AI agents introduce a level of autonomy and reasoning. They can act on ambiguous instructions (e.g. “find the best contacts to reach out to”) and pulling in data from various systems (like MadKudu) to make decisions.

Example: an AI agent drafting emails decide on its own what to write in this email. While a non-AI workflow would use a template text with placeholders and the email would look similar for all recipients.

AI agents are not products—they are custom-built automations. And like any automation, when something goes wrong, it’s important to trace which part of the system is responsible

🧱 The 4 Components of Any AI Agent

To build a working AI agent using MadKudu data, you need three components:

1. The Orchestration Platform

This is where the agent logic lives and gets executed. It’s the engine that chains steps together.

Example: n8n, Make, Zapier, Dust, custom code running in Cursor ... These platforms execute the flow: pulling data, making decisions, sending messages, etc.

2. The LLM

This is the “thinking” layer of your agent.

You can use: OpenAI, Claude, Mistral models, any model you want

3. The Prompt

The prompt contains the instructions in plain english that will be interpreted by the LLM

Example:

• "Find contacts at {company}"

• "Draft an email for {email} using all the information you know from MadKudu"

LLMs are powerful but inherently unpredictable. They can hallucinate, misinterpret instructions, or fail entirely when given poorly structured inputs. When building agents, much of the effort goes into refining prompts and selecting the right model to minimize the risk of misinterpretation and ensure more consistent and reliable outputs.

4. The Data layer: MadKudu MCP

MadKudu provides the data layer—the context that inform the agent’s decisions.

Through the MadKudu Public API and MCP integration, you can

• get information about your accounts and contacts: firmographics, demographics, scores, engagement activities with your website, product, marketing, job history...

• search for accounts and contacts

• source and enrich net new contacts

🧪 Example Use Case: Meeting Prep Agent

Here’s how a MadKudu-powered AI agent for meeting prep might be built:

• Orchestration: n8n monitors your calendar, detects meetings with prospects.

• LLM: GPT-4 summarizes MadKudu data and past emails into a briefing doc.

• MadKudu: The MCP provides the account brief, top users, recent activity, and firmographics.

You decide when the agent runs, what it generates, and where it shows up (Notion, Slack, Google Docs…).

MadKudu helps power your AI agents by exposing real-time GTM data through secure APIs and the Model Context Protocol (MCP). But we don’t control:

• Which tool you use to orchestrate

• How you write your prompts

• How your model interprets the data

Treat MadKudu as your intelligent data layer—not the AI agent itself.