LLM Agents, Part 4 - Workflow to Multi-agent Architecture Design

BACKGROUND

To understand this better, feel free to skim the previous writeups first;

TRANSCRIPT SUMMARY:

Here our focus would be to transform a regular sales workflow of a Bio Tech Sales company into a modular, maintainable. and a scalable multi-agent architecutre. We’ll explore how we can utilize a traditional architecture design called Service-Oriented Architecture (SOA) to create a sophisticated multi-agent system.

Current Workflow of Bio Tech Sales Scenario

Before we dive into architectural patterns, it's crucial to understand the current process we're working with. Let's first break down the sales workflow of Bio Tech Sales company:

1. The founder identifies potential pharmaceutical company leads

2. A sales engineer evaluates the leads and asks clarifying questions

3. The technical lead suggests relevant use cases, and the founder estimates ROI

4. The sales engineer assesses the feasibility of use cases with the tech lead

5. A proposal is drafted, refined, and sent to the pharma company prospect

6. The prospect's team (medical scientist, chemist, CFO) reviews the proposal

7. Clarifications and objection handling occur via email

8. A meeting is scheduled if the proposal is accepted

Service-Oriented Architecture

Before we apply SOA to our biotech sales scenario, let's briefly recap what it is and why it matters.

Service-Oriented Architecture (SOA) is a design approach used in traditional software architectures to break down complex systems into independent, modular services that communicate through standardized interfaces. These services are designed to perform specific business functions while remaining loosely coupled and reusable, allowing for greater flexibility and scalability across the system. SOA enables different services to operate autonomously, while still being able to collaborate when needed, ensuring that systems can evolve and adapt without requiring a complete overhaul.

You can read about SOA in detail here.

Applying SOA to the Biotech Sales System

With a better understanding of SOA in traditional software architectures, let's now apply these concepts to our biotech sales scenario.

In order to identify our key agents, let’s break down the workflow into discrete, reusable services grouped together based on theme or domain of work. This approach will allow for greater flexibility, easier maintenance, and improved scalability.

Once we have the grouping based on our workflow analysis, we can identify three key agents:

1. Business Development Agent

2. Sales Agent

3. Customer Simulation Agent

These agents will act as logical groupings of related services, each responsible for a specific aspect of the sales process. We're also keeping a human in the loop - the founder - who will provide critical inputs and oversight.

Business Development Agent Services

1. Lead Generation Service: Identifies potential pharma company leads.

2. Lead Qualification Service: Evaluates and qualifies the identified leads.

3. Viability Assessment Service: Assesses the viability of pursuing a lead, including suggesting relevant use cases and estimating ROI.

4. Objection Handling Service: Manages clarifications and objections raised by prospects.

5. Meeting Scheduling Service: Arranges meetings with interested prospects.

Sales Agent Services

1. Feasibility Assessment Service: Evaluates the technical feasibility of proposed solutions, working closely with the technical lead.

2. Proposal Generation Service: Drafts, refines, and sends proposals to pharma company prospects.

Customer Simulation Agent Service

1. Proposal Review Service: Simulates the review process by the pharma company's medical scientist, chemist, and CFO.

Each of these services follows SOA principles with standardized interfaces, loose coupling, abstracted internal complexity, and potential reuse in different contexts.

Walkthrough of Service Oriented System

Let's walk through how this SOA-based system would handle a typical sales process:

1. The process begins with the Business Development Agent's Lead Generation Service identifying potential pharma company leads using data from sources like Crunchbase and PitchBook.

2. The Lead Qualification Service then evaluates these leads, possibly using a machine learning model to score them based on predefined criteria.

3. For qualified leads, the Viability Assessment Service kicks in, suggesting relevant use cases and estimating ROI. This service might use a combination of historical data and AI-driven forecasting.

4. The system notifies the founder (our human-in-the-loop) via Slack, presenting the qualified leads and viability assessments. The founder can provide feedback, add additional insights, or approve moving forward.

5. Once approved, the Sales Agent's Feasibility Assessment Service evaluates the technical feasibility of the proposed solutions. This might involve analyzing technical requirements and consulting internal knowledge bases.

6. The Proposal Generation Service then creates a tailored proposal based on all the gathered information. This could involve using templates and AI-driven customization.

7. The generated proposal is sent to the Customer Simulation Agent's Proposal Review Service, which simulates the review process of the pharma company's team. This service might use NLP to analyze the proposal and generate realistic objections based on historical data.

8. Any objections or requests for clarification are handled by the Business Development Agent's Objection Handling Service, which might use a combination of pre-defined responses and AI-generated explanations.

9. If the simulated customer is satisfied, the Meeting Scheduling Service arranges a follow-up meeting, integrating with calendar systems like Google Calendar or Outlook.

Throughout this process, the system maintains a shared memory where all actions and important data are recorded. This allows for better coordination between services and provides a clear audit trail.

Benefits of This SOA Approach

By applying SOA principles to our biotech sales system, we gain several advantages:

• Modularity: Each service can be developed, tested, and maintained independently. If we need to update our lead scoring algorithm, we can do so without touching the proposal generation system.

• Scalability: Individual services can be scaled based on demand. If we're seeing a surge in lead generation, we can allocate more resources to that service without affecting others.

• Flexibility: New services can be added or existing ones modified as business needs evolve. For instance, if we later want to add a pricing optimization service, we can do so without overhauling the entire system.

• Reusability: Services like Objection Handling could potentially be reused in different contexts, not just in initial sales but also in account management.

• Improved Efficiency: By automating many of the time-consuming aspects of the sales process, we free up human resources to focus on high-value activities like relationship building and strategic decision-making.

Conclusion

In this part of our multi-agent series, we demonstrated how to transform a real-world workflow into a modular and scalable multi-agent system using Service-Oriented Architecture (SOA). By breaking down complex processes into discrete and reusable services, we created a flexible system that can adapt easily without any major overhauls.

Next, we'll explore how these services interact with each other, while examining the communication patterns that enable collaboration between agents.