---
title: "Customer Acquisition and Management Agent Architecture"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Customer Acquisition and Management Agent Architecture

Let’s design a **Customer Acquisition and Management Agent** for a business leveraging LinkedIn to identify potential customers, approach them with tailored outreach, sell products/services, and manage ongoing relationships. This agent will monitor LinkedIn activity, suggest prospects and messages, notify sales teams for approval, and automate follow-ups and relationship nurturing. I’ll include additional automation features and identify where **Large Language Model (LLM) intelligence** can enhance functionality. First, I’ll outline the requirements, then detail the implementation using **event-driven architecture (EDA)**, **three-tier microservices architecture**, **stateless computing**, **scheduled computing (CronJobs)**, and **human-in-the-loop (HITL)**.

---

### Requirements for the Customer Acquisition and Management Agent (LinkedIn)

#### Functional Requirements
1. **Customer Identification**:
   - Monitor LinkedIn for potential customers based on profiles (e.g., job titles, industries, interests) and activity (e.g., posts, comments).
   - Suggest prospects matching the company’s target audience (e.g., "IT Manager at TechCorp").
   - Notify sales teams with prospect details for outreach approval.

2. **Outreach and Sales**:
   - Suggest personalized connection requests, messages, or InMails to engage prospects.
   - Notify sales reps with proposed outreach content for approval before sending.

3. **Relationship Management**:
   - Track interactions (e.g., messages, meetings) and suggest follow-ups or nurturing actions (e.g., "Share industry article").
   - Notify sales reps to maintain relationships with prospects/customers.

4. **Manual Sales Requests**:
   - Allow sales reps to request outreach campaigns (e.g., "Target 50 CFOs") or specific actions (e.g., "Follow up with John Doe").
   - Agent optimizes requests (e.g., refines target list, crafts messages) and seeks approval.

#### Additional Automation Requirements
5. **Lead Scoring**:
   - Automatically score prospects based on engagement, profile fit, and intent signals (e.g., liking a product-related post).
6. **Content Sharing**:
   - Suggest and automate posting of company content (e.g., blogs, case studies) to attract prospects.
7. **Competitor Analysis**:
   - Monitor competitor activity on LinkedIn and suggest counter-strategies (e.g., "Outreach to their clients").
8. **CRM Integration**:
   - Sync prospect and customer data with a CRM (e.g., Salesforce) for seamless management.

#### Non-Functional Requirements
1. **Scalability**: Handle multiple sales reps and thousands of prospects.
2. **Real-Time**: Identify and engage prospects promptly.
3. **Reliability**: Ensure accurate prospect targeting and message delivery.
4. **Usability**: Provide an intuitive interface for sales teams.
5. **Security**: Protect LinkedIn credentials and customer data (e.g., OAuth, encryption).

#### User Stories
- As a sales rep, I want LinkedIn prospects identified to focus my efforts on high-potential leads.
- As a sales manager, I want automated outreach messages to save time on initial contact.
- As a business owner, I want relationship nurturing suggestions to retain customers long-term.

---

### Implementation Using the Defined Architecture

#### Architecture Overview
- **Three-Tier**: Presentation (UI for sales reps), Business Logic (agent processing), Data (prospect and interaction data).
- **EDA**: Events drive prospect identification, outreach, and relationship management.
- **Stateless Computing**: Scalable processing of tasks.
- **CronJobs**: Periodic prospect scans and analytics.
- **HITL**: Sales reps approve actions.

---

#### Components and Workflow

##### 1. Three-Tier Architecture
- **Presentation Layer**:
  - **Sales Dashboard**: View prospect lists, suggested outreach messages, relationship actions, analytics, and submit requests.
  - Notifications (e.g., email, app alerts) for new prospects or follow-up reminders.
- **Business Logic Layer**:
  - **Prospect Identification Agent**: Monitors LinkedIn for potential customers, suggests prospects.
  - **Outreach Agent**: Crafts connection requests and messages for sales outreach.
  - **Relationship Agent**: Suggests follow-ups and nurturing actions.
  - **Request Optimizer**: Optimizes manual sales requests.
  - **HITL Coordinator**: Manages approval workflows.
- **Data Layer**:
  - Stores:
    - Prospect data (e.g., LinkedIn ID, job title, company, score).
    - Interaction history (e.g., messages, responses).
    - Suggested actions and approval status.
  - Tools: Database (PostgreSQL), cache (Redis) for real-time data.

##### 2. Event-Driven Architecture
- **Event Types**:
  - `ProspectDetected`: New potential customer identified.
  - `OutreachSuggested`: Connection request or message proposed.
  - `RelationshipActionSuggested`: Follow-up or nurturing action proposed.
  - `HumanReviewRequired`: Approval needed.
  - `HumanResponseReceived`: Sales rep approves/modifies/rejects.
  - `ActionExecuted`: Action completed (e.g., message sent).
- **Event Bus**: Kafka for event routing.
- **Workflow**:
  1. `ProspectDetected` → Outreach Agent suggests message → `OutreachSuggested`.
  2. `OutreachSuggested` → `HumanReviewRequired` → Sales rep approves → `ActionExecuted`.
  3. `RelationshipActionSuggested` → Sales rep approves → Follow-up sent.

##### 3. Stateless Computing
- **Prospect Processor**: Stateless service (Lambda) processes LinkedIn data, emits `ProspectDetected`.
- **Outreach Processor**: Stateless function crafts messages, emits `OutreachSuggested`.
- **Relationship Processor**: Stateless service suggests nurturing actions.
- **HITL Handler**: Stateless service manages approvals.
- **Action Executor**: Stateless function sends messages via LinkedIn API or updates CRM.

##### 4. Scheduled Computing (CronJobs)
- **Prospect Scanner**: Daily scan of LinkedIn for new prospects → `ProspectDetected`.
- **Lead Score Updater**: Daily job recalculates prospect scores → Updates data layer.
- **Content Poster**: Weekly job posts company content → `ActionExecuted`.

##### 5. Human-in-the-Loop (HITL)
- **Outreach Approval**: `OutreachSuggested` (e.g., "Connect with Jane, IT Manager") → Sales rep approves → `ActionExecuted`.
- **Relationship Actions**: `RelationshipActionSuggested` (e.g., "Share blog with John") → Sales rep approves → Sent.
- **Manual Requests**: Sales rep requests "Target 20 CEOs" → Request Optimizer refines → Sales rep approves → Campaign executed.

---

#### Areas for LLM Intelligence
1. **Outreach Messages (Business Logic - Outreach Agent)**:
   - **Use Case**: Craft personalized connection requests or InMails (e.g., "Hi Jane, I noticed your work in IT at TechCorp—our solution could streamline your operations!").
   - **LLM Role**: Generate natural, persuasive messages tailored to prospect profiles.
   - **Implementation**: LLM processes `ProspectDetected`, outputs to `OutreachSuggested`.

2. **Relationship Nurturing (Business Logic - Relationship Agent)**:
   - **Use Case**: Suggest follow-up messages (e.g., "Hey John, thought you’d enjoy this article on cloud security").
   - **LLM Role**: Create engaging, context-aware content based on interaction history.
   - **Implementation**: LLM enhances `RelationshipActionSuggested`.

3. **Prospect Insights (Presentation Layer - Sales Dashboard)**:
   - **Use Case**: Explain why a prospect was selected (e.g., "Jane’s recent post on cybersecurity aligns with our product").
   - **LLM Role**: Translate raw data (e.g., profile, posts) into readable insights.
   - **Implementation**: LLM adds explanations to `ProspectDetected` notifications.

4. **Content Creation (Business Logic - Content Poster)**:
   - **Use Case**: Generate LinkedIn posts to attract prospects (e.g., "Boost your team’s productivity with these 5 tips!").
   - **LLM Role**: Craft compelling, industry-relevant content.
   - **Implementation**: LLM outputs to `ActionExecuted` for CronJob posts.

5. **Competitor Analysis Responses (Business Logic - Prospect Processor)**:
   - **Use Case**: Suggest counter-messages to competitor activity (e.g., "Unlike Competitor X, we offer 24/7 support").
   - **LLM Role**: Analyze competitor posts and craft strategic responses.
   - **Implementation**: LLM enhances `OutreachSuggested` with competitive edge.

---

#### Detailed Implementation

##### Step 1: Customer Identification
- **Tech**: LinkedIn API (e.g., Search API, Profile API).
- **Flow**:
  - IT Manager posts about tech needs → `ProspectDetected {prospectId, jobTitle, company}`.
  - Prospect Processor scores lead (80/100) → Stored in data layer.

##### Step 2: Outreach and Sales
- **Tech**: Outreach Processor with LLM.
- **Flow**:
  - `ProspectDetected` → LLM: "Hi Jane, loved your post on IT challenges—can we connect?" → `OutreachSuggested`.
  - Sales rep approves → `ActionExecuted` → Message sent via LinkedIn.

##### Step 3: Relationship Management
- **Tech**: Relationship Processor with LLM.
- **Flow**:
  - Jane responds → `RelationshipActionSuggested` → LLM: "Great to connect, Jane! Here’s a resource on IT efficiency" → Sales rep approves → Sent.

##### Step 4: HITL for Approvals
- **Tech**: Dashboard + HITL Handler.
- **Flow**:
  - `HumanReviewRequired {task: "Send InMail to Jane"}` → Sales rep approves → `ActionExecuted`.

##### Step 5: Manual Requests
- **Tech**: UI + Request Optimizer with LLM.
- **Flow**:
  - Sales rep: "Target 10 CFOs" → LLM suggests: "Focus on finance sector, message: ‘Boost ROI with us’" → `HumanReviewRequired` → Approved → Campaign sent.

##### Step 6: Automation Features
- **Lead Scoring**: Daily job updates scores → High-scorers prioritized.
- **Content Sharing**: Weekly post → LLM: "Top trends in tech this month" → Posted.
- **CRM Sync**: Prospect added → Synced to Salesforceautomatically synced to Salesforce.
- **Competitor Analysis**: Competitor posts → LLM: "Highlight our unique feature" → `OutreachSuggested`.

---

#### Example Workflow
1. **Prospect Identification**:
   - CFO posts about budgeting → `ProspectDetected` → LLM: "Hi Mark, our tool can optimize your budget—connect?" → Sales rep approves → Sent.
2. **Outreach**:
   - Mark accepts → `RelationshipActionSuggested` → LLM: "Thanks for connecting, Mark! Here’s a case study" → Approved → Sent.
3. **Relationship Management**:
   - Mark engages → `RelationshipActionSuggested` → LLM: "Mark, let’s discuss your needs—free demo?" → Approved → Meeting scheduled.
4. **Manual Request**:
   - Sales rep: "Target 5 HR leads" → LLM optimizes: "Focus on recent HR posters" → Approved → Messages sent.

---

### Benefits
- **Real-Time**: EDA ensures instant prospect detection and outreach.
- **Scalable**: Stateless services handle large-scale LinkedIn activity.
- **Engagement**: LLM crafts compelling, personalized messages.
- **Efficiency**: Automation streamlines lead nurturing and CRM updates.

### Challenges
- **LLM Tone**: Messages must align with brand voice and professionalism.
- **LinkedIn Limits**: API rate limits and usage policies may constrain actions.

This Customer Acquisition and Management Agent leverages LinkedIn to grow and nurture a customer base with automation and LLM intelligence, enhancing sales efficiency.
