AI SDR system

Concepts covered

Step breakdown

1. Define qualificationAgree on the fields, scoring, stage, owner, and handoff rule Sales expects.
2. Create the agent and workflowUse Agent Builder for extraction and Workflow Builder for routing, CRM updates, and follow-up.
3. Test sample leadsRun qualified, nurture, and missing-context samples through the workflow.
4. Review with SalesCheck CRM records, messages, assignments, activity, logs, and analytics before publishing.

What you will build

You will build a sales development system that captures a lead, qualifies intent, updates CRM, sends a channel follow-up, creates a handoff for Sales, and leaves analytics and activity history.

Use sample leads and demo customer data only. The system should teach the operating pattern without exposing customer records.

When to use it

Use this blueprint when inbound messages or forms need fast qualification before a sales teammate spends time on them.

It works best when the qualification criteria, CRM fields, handoff rule, and follow-up channel are already agreed with Sales.

Before you start

Define lead source, required qualification fields, score or stage rules, CRM object mapping, sales owner assignment, follow-up message style, and escalation criteria.

Prepare sample lead records, sample conversations, and placeholder API values if the workflow calls external systems.

Step-by-step implementation

Create or review a Sales Qualification Agent in Agent Builder with instructions for extracting name, company, role, need, urgency, timeline, and next step.

Create a workflow with a lead trigger from a channel, form, webhook, or CRM event.

Add CRM lookup or create/update nodes for Person, Company, and Deal records.

Pass the lead context into the AI agent and return structured qualification fields.

Route qualified, nurture, missing-context, and handoff cases into separate branches.

Send the approved channel follow-up, assign Sales when needed, create Max Activity, and verify Logs and Analytics before moving Live.

How to verify it worked

Run a sample lead end-to-end and confirm the Person, Company, and Deal fields, owner assignment, follow-up message, activity history, logs, and analytics are correct.

Run a low-fit or missing-context sample and confirm it does not create a false sales-ready handoff.

Common mistakes

Do not qualify every lead as sales-ready because the first reply was positive.

Do not let the AI agent invent missing company, budget, or timing details.

Do not skip the CRM update; Sales needs the context after the handoff.

Troubleshooting

If qualification is generic, pass clearer CRM fields and sample conversation context into the agent step.

If duplicate records appear, review CRM lookup keys before create/update nodes.

If Sales does not receive the handoff, check assignment, notification, Max Activity, and branch conditions.

Related workflows

Build a workflow for the routing and CRM update path.

Connect APIs if enrichment, calendar, or external routing systems need to participate.

Clear next step

Build the first sample run with demo lead data, review it with Sales, then add only the channel and enrichment steps that are required for launch.

Operational playbook

Build this as a small deliverable: define the trigger, source data, owner, expected output, and the exact place the team will review it.

For AI SDR system, keep the first version narrow enough that a teammate can test it end to end before expanding it into a broader Sales system.

Best practices

Start with the operational job before changing configuration. Name the owner, define the trigger or source context, and decide how the result should be reviewed.

Start with one workflow, message, record update, or Max task before expanding. The team should know what changed, who owns it, and how to pause or adjust it.

Platform layers involved

Studio defines the workflow and AI agent behavior. Channels capture the customer interaction. CRM provides customer memory. Max Activity shows what the system did and what needs follow-up.

Use the solution page as the business-facing map, then open the related product tutorials when you need configuration detail.

Outcome metrics

Track a small set of operational signals: response time, handoff rate, completion rate, escalation quality, CRM field completeness, reply rate, and repeated failure patterns.

The metric should reflect the business outcome, not only whether the automation ran.

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