AI Automation: Scaling Digital Shelf Optimization with distributed multi-agent system

A team of researchers from Hong Kong Polytechnic University has introduced LAMBDA, a new code-free multi-agent data analysis system developed to overcome the lack of effective communication between domain experts and advanced AI models.

Ever wondered how such a novel concept could find a way to solve a real business problem for your Digital Shelf Content?

If you're like most, you've probably wrestled with questions like, "How can I efficiently manage my digital shelf content for the volume of my product range across all marketplaces?" or "What tech can I use to automate my digital shelf content to stay agile and competitive?" 

Well, let’s dive into a refreshingly new approach: how distributed multiagent systems can be the game-changer in this space.

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What are the distributed multiagent systems for AI Automation?

Distributed multi-agent systems are the new ways of breaking down a process with smaller steps managed by special AI agents. 

I have written a lot about the business process of managing the digital shelf content. Let me dive a little deeper into the technical aspects on how the system really works:

Layered Protocols

1. Low-Level Network: This layer handles the basic communication infrastructure, ensuring that agents can interact smoothly across the network.

2. Mid-Level Data: At this level, data is managed and transferred between agents, facilitating the sharing of insights and information necessary for Digital Shelf Optimization.

3. High-Level Conversation and Context: This layer focuses on the context and dialogue between agents, enabling them to collaborate effectively and make informed decisions.

Specialized Agents for Targeted Tasks

Each agent is designed to perform a specific task within the Digital Shelf Optimization process.

Types of Agents:

• LLM Agents: Leveraging large language models, these agents handle tasks that involve natural language processing and content generation.

• Deterministic Agents: Representing existing technology components and software algorithms, these agents perform tasks based on predefined rules and logic.

• Human Assistant Agents: These agents simulate human decision-making and support tasks that require a human touch.

Distributed Infrastructure 

• Networked Distribution: The system operates over a distributed network, allowing agents to function independently while remaining interconnected.

• Communication Protocols: Standardized protocols ensure that agents can communicate effectively, sharing data and insights seamlessly.

• Discovery and Context Management: Agents can discover and adapt to new contexts.

• Guardrails for Security and Process Integrity: Built-in safeguards ensure that agents operate within defined parameters, maintaining the integrity and security of the optimization process.

Process Orchestrator 

• Business Process Implementation: The orchestrator leverages distributed agents to execute complex business processes, coordinating their efforts across the digital shelf content life cycle.

• Service Bus Integration: By using a service bus, the orchestrator facilitates communication and data exchange between agents.

• Context Management: The orchestrator manages the overall context in which agents operate, ensuring that they have the necessary information to perform their tasks effectively.

• Security and Guardrails Implementation: The orchestrator enforces security protocols and operational guardrails, protecting the system from potential threats and ensuring compliance with business rules.

At Genrise.ai, we are working on implementing this concept for digital shelf optimization. Our multi-agent orchestrator automates digital shelf content optimization using multiple types of AI agents.

Here’s how it works:

1. Keywords filtering agent: Focuses on collecting and filtering the right keywords for your product and brand. 

2. Content Ranking agent: Identify low performers using our advanced deterministic algorithm.

3. Brand Copy generator: Generate new product copies that align with your brand voice, maintaining consistency across all marketplaces.

4. Retailers Copy Generator: Create multiple copies tailored to specific retailer guidelines, ensuring compliance and optimal performance.

5. Claim Review: Automatically reviews content against your claim database, ensuring accuracy and legal compliance.

6. Human Assist: Include a human review step before final publication, ensuring learnings are available for the future iterations.

Scaling Digital Shelf Optimization doesn't have to be a Herculean task. With Distributed AI agents systems, you can automate your efforts - like never before, and scale across the entire portfolio of your products and marketplaces.