Overview
Multi-agent sales systems automate lead qualification and nurturing, allowing human sales reps to focus on high-value activities. Agents handle research, outreach, qualification, and CRM management.
Architecture
New Lead → Lead Scoring Agent → Prioritized Queue
↓
Research Agent → Lead Profile
↓
Outreach Agent → Initial Contact
↓
Qualification Agent → Qualified/Not
↓
Nurturing Agent → Engagement Sequence
↓
Meeting Scheduler Agent → Human Handoff
↓
CRM Update Agent → System of Record
Agent Roles
Lead Scoring Agent
- Evaluates lead quality signals
- Prioritizes based on fit and intent
- Segments for appropriate treatment
Research Agent
- Gathers company and contact information
- Identifies pain points and use cases
- Finds relevant connections
Outreach Agent
- Crafts personalized messages
- Manages multi-channel outreach
- Handles initial responses
Qualification Agent
- Assesses BANT criteria (Budget, Authority, Need, Timeline)
- Identifies decision-makers
- Validates fit
Nurturing Agent
- Maintains engagement over time
- Shares relevant content
- Tracks interest signals
Meeting Scheduler Agent
- Finds mutual availability
- Handles rescheduling
- Manages calendar integration
CRM Update Agent
- Logs all activities
- Updates contact records
- Maintains data hygiene
Results in Practice
AI agents are used to qualify leads, manage customer interactions, analyze sentiment, and perform competitive research at scale, freeing human sales reps for relationship-building and closing.
Key Patterns
- Supervisor Pattern: Lead scoring orchestrates flow
- Human-in-the-Loop: Handoff to human for closing
- Tool Use Pattern: CRM, email, calendar integrations
Failure Modes
- Over-Automation: Prospects feel like they're talking to bots
- Data Quality: Garbage in, garbage out in scoring
- Context Loss: Previous interactions not properly surfaced
- Compliance Risk: Email regulations, privacy laws