Ultimate AI SDR
Building a sophisticated AI SDR agent leveraging internet search and evals to draft personalized outreach emails in 15 seconds
The Problem: Generic Sales Outreach Doesn’t Work
Dear John,
I hope this email finds you well. I wanted to reach out about our security services that might be of interest to YMU Talent Agency.
Our company provides security personnel for events. We have many satisfied customers and would like to schedule a call to discuss how we can help you.
Let me know when you’re available.
Regards,
Sales Rep
Subject: Quick security solution for YMU’s talent events
Hi John,
I noticed YMU’s been expanding its roster of A-list talent lately – congrats on that growth. Having worked event security for talent agencies before, I know how challenging it can be coordinating reliable security teams, especially on short notice.
We’ve built something I think you’ll find interesting – an on-demand security platform that’s already being used by several major talent agencies.
Best,
Ilya
This cookbook shows you how to build an AI-powered system that:
- Researches prospects in real-time using up-to-date web data
- Crafts personalized emails based on prospect-specific insights
- Self-evaluates and improves its output before sending
- Scales to thousands of prospects at a fraction of the usual cost
Multi-Agent Architecture
Our system combines three specialized AI models:
- Orchestrator (Claude 3.7): Generates research queries, drafts emails, and refines based on feedback
- Researcher (Perplexity): Gathers real-time web information about prospects and companies
- Evaluator (OpenAI): Reviews email quality, providing scores and improvement suggestions
This architecture delivers superior results because:
- Each model handles tasks it excels at
- The system includes built-in quality control
- Cost efficiency through right-sized models and targeted research
Creating the Prompt Templates
What You’ll Create
The Orchestrator template handles three different roles depending on which “mode” is activated:
- Research Query Generator: Creates targeted questions for the researcher
- Email Drafter: Uses research findings to write personalized outreach
- Email Refiner: Incorporates evaluator feedback to improve the email
Variables You’ll Need
| Variable | Purpose | Example |
|---|---|---|
our_offering | Your product/service description | ”Umbrella Corp offers ‘Uber for personal protection’…” |
company_name | Prospect’s company | ”YMU Talent Agency” |
company_industry | Industry sector | ”Elite Talent Management” |
target_person_name | Contact name | ”John Wick” |
target_person_designation | Contact’s role | ”Event Organizer” |
requirement_gathering_mode | Activates research query mode | ”TRUE” or "" (empty) |
research_mode | Activates email drafting mode | ”TRUE” or "" (empty) |
evaluator_mode | Activates email refinement mode | ”TRUE” or "" (empty) |
researcher_output | Data from the researcher | (JSON response from research) |
evaluator_output | Feedback from evaluator | (JSON with score and comments) |
Step-by-Step Setup
- Create template in prompt.new with Claude 3.7 Sonnet
- Add core partials:
Let’s create reusable components that define our SDR’s core instructions and persona. These are added as Prompt Partials - reusable blocks that can be inserted in any template.
We’ll insert both partials into the template’s system role like this:
- Add product offering:
Next, we’ll add a section that will receive your company’s offering details from a variable:
We’ll send this variable’s content at runtime.
- Add Prospect Information Section:
Now let’s add a section that will receive the prospect information variables:
We’ll send these values at runtime as well.
- Create Agent-Specific Sections with Conditional Logic:
This is where the magic happens! We’ll add three “conditional sections” that only appear when a specific mode is activated:
A. Research Query Generation Mode: Here, we’ll explain how the research query should be generated.
At this stage, we can send a request to the researcher get the research output back.
B. Email Drafting Mode (add this section next):
Once we have the research output, we can create the first email, and add the following to a new user role in the prompt template:
We’ll take this email and send it to the evaluator, which will send back a JSON with two keys: “score” and “comment”.
C. Email Refinement Mode (add this final section):
With the Evaluator’s output, we’ll now create the final email.
The Power of Conditional Variables
This approach with {{#variable_name}} syntax lets you use a single template for three different purposes. When you set requirement_gathering_mode to “TRUE”, only that section appears. When you set it to empty and instead set research_mode to “TRUE”, the email drafting section appears instead. This keeps your templates DRY (Don’t Repeat Yourself).
Complete Template Overview
When finished, your template should have:
- Core instruction and persona partials at the top
- Company offering section
- Prospect information section
- Three conditional sections for different modes
This single template will now handle all three stages of the orchestrator’s job, activated by different variables in your code.
What You’ll Create
The Orchestrator template handles three different roles depending on which “mode” is activated:
- Research Query Generator: Creates targeted questions for the researcher
- Email Drafter: Uses research findings to write personalized outreach
- Email Refiner: Incorporates evaluator feedback to improve the email
Variables You’ll Need
| Variable | Purpose | Example |
|---|---|---|
our_offering | Your product/service description | ”Umbrella Corp offers ‘Uber for personal protection’…” |
company_name | Prospect’s company | ”YMU Talent Agency” |
company_industry | Industry sector | ”Elite Talent Management” |
target_person_name | Contact name | ”John Wick” |
target_person_designation | Contact’s role | ”Event Organizer” |
requirement_gathering_mode | Activates research query mode | ”TRUE” or "" (empty) |
research_mode | Activates email drafting mode | ”TRUE” or "" (empty) |
evaluator_mode | Activates email refinement mode | ”TRUE” or "" (empty) |
researcher_output | Data from the researcher | (JSON response from research) |
evaluator_output | Feedback from evaluator | (JSON with score and comments) |
Step-by-Step Setup
- Create template in prompt.new with Claude 3.7 Sonnet
- Add core partials:
Let’s create reusable components that define our SDR’s core instructions and persona. These are added as Prompt Partials - reusable blocks that can be inserted in any template.
We’ll insert both partials into the template’s system role like this:
- Add product offering:
Next, we’ll add a section that will receive your company’s offering details from a variable:
We’ll send this variable’s content at runtime.
- Add Prospect Information Section:
Now let’s add a section that will receive the prospect information variables:
We’ll send these values at runtime as well.
- Create Agent-Specific Sections with Conditional Logic:
This is where the magic happens! We’ll add three “conditional sections” that only appear when a specific mode is activated:
A. Research Query Generation Mode: Here, we’ll explain how the research query should be generated.
At this stage, we can send a request to the researcher get the research output back.
B. Email Drafting Mode (add this section next):
Once we have the research output, we can create the first email, and add the following to a new user role in the prompt template:
We’ll take this email and send it to the evaluator, which will send back a JSON with two keys: “score” and “comment”.
C. Email Refinement Mode (add this final section):
With the Evaluator’s output, we’ll now create the final email.
The Power of Conditional Variables
This approach with {{#variable_name}} syntax lets you use a single template for three different purposes. When you set requirement_gathering_mode to “TRUE”, only that section appears. When you set it to empty and instead set research_mode to “TRUE”, the email drafting section appears instead. This keeps your templates DRY (Don’t Repeat Yourself).
Complete Template Overview
When finished, your template should have:
- Core instruction and persona partials at the top
- Company offering section
- Prospect information section
- Three conditional sections for different modes
This single template will now handle all three stages of the orchestrator’s job, activated by different variables in your code.
What You’ll Create
The Researcher template powers the real-time web research capabilities of your AI SDR system.
Variables
| Inputt | Description | Source/Destination |
|---|---|---|
requirement_gathering_output | Research queries | Received from Orchestrator |
Setup Steps
-
Create a new prompt template with Perplexity Sonar Pro as the model
-
Add researcher instructions:
Add these system instructions that define the researcher’s role:
Your completed template should look like this:
What You’ll Create
The Evaluator template provides quality control for your AI SDR system.
Variables
| Input | Description | Source/Destination |
|---|---|---|
work_history | Research queries + findings + email draft | Received from previous steps |
Setup Steps
-
Create a template with OpenAI o3-mini as the model
-
Add evaluator instructions:
Add these system instructions that define the evaluator’s role:
Your completed template should look like this:
Evaluator is like an AI sales manager reviewing drafts before they go out - ensuring consistent quality at scale.
Implementing the Workflow
Setup
Generate Research Queries
Conduct Research
Draft Initial Email
Evaluate Email
Refine Email
Monitoring and Optimization
Portkey’s trace view provides complete visibility to track performance, cost, latency, and opportunities for improvement.
Implementation Checklist
✅ Set up Portkey account and API credentials
✅ Create prompt templates for all three agents
✅ Define your company offering and SDR persona
✅ Configure basic prospect information
✅ Implement the five-step workflow
✅ Set up tracing and monitoring
✅ Create a system for batching multiple prospects
Troubleshooting & Best Practices
| Issue | Solution |
|---|---|
| Low research quality | Make research queries more specific |
| Generic emails | Ensure research findings are prominently featured |
| High token usage | Remove redundant information from prompts |
Ready to Transform Your Outreach?
This AI SDR system isn’t just an incremental improvement—it’s a fundamental reimagining of how sales development works. By combining specialized AI agents in an orchestrated workflow, you can achieve personalization at scale that was previously impossible.
The result? More meetings, stronger relationships, and ultimately more closed deals—all while freeing your team to focus on high-value activities.
Was this page helpful?