Developing Dynamic AI Agents and Agent Graphs for Personalized Customer Engagement

Case Study 

Date - 11.02.2025                                                                                                                   4 mins read​

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Business Problem

Modern businesses struggle with effectively utilizing customer feedback to provide personalized offers, discounts, and incentives aimed at maximizing sales and customer satisfaction. Traditional recommendation systems often lack the ability to dynamically adapt to customer preferences, leading to inefficient marketing strategies. Some customers may respond better to discounts, while others prefer gift coupons or loyalty points.

We studied the business problem and proposed a multi-agent system was needed to intelligently process customer feedback, analyze purchasing behavior, and dynamically select the most effective engagement strategy. The objective was to maximize customer retention, reordering rates, and revenue growth, leveraging AI-driven recommendations tailored to each individual customer.

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Solution: AI-Driven Multi-Agent System

To achieve this, we developed a multi-agentic system that utilizes a combination of Machine Learning (ML) models, Reinforcement Learning (Q-Learning/Multi-Agent Reinforcement Learning - MARL), and LLMs to dynamically invoke specialized agents. The system uses LangChain's Agent Graph for real-time decision-making and ensures personalized offers are sent based on customer behavior and sentiment analysis.

 

Technical Implementation

The system was architected using the following technologies:

• Azure OpenAI & Locally Trained LLMs (DeepSeek-Tiny, Gemma-2B) for conversational intelligence and context understanding.

• DistilBERT for sentiment classification to gauge customer satisfaction.

• Multi-ML Models, including Gradient Boosting Decision Trees (GBDT) for customer segmentation, Transformer-based models for sentiment analysis, Neural Collaborative Filtering (NCF) for recommendation, and Reinforcement Learning (Q-Learning/MARL) for offer optimization for customer profile analysis and purchasing behavior prediction.

• Q-Learning & MARL for dynamic agent invocation based on engagement effectiveness.

• CosmosDB for storing customer interactions, historical feedback data, and real-time engagement metrics. To enhance retrieval efficiency, embeddings were created from textual feedback using Sentence Transformers, enabling similarity-based searches and contextual recommendations.

• FastAPI for managing agent communications in a distributed environment.

• T4 for email summarization and other efficiency improvements to optimize engagement processing and offer personalization.

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1. Dynamic Agent Invocation Based on Customer Behavior

When customer feedback is received, multiple agents are activated:

Purchasing Behavior Analysis Agent - Determines previous buying patterns.

Offer Selection Agent - Fetches the most suitable offer (discount, coupon, loyalty points, etc.).

Sentiment Analysis Agent - Evaluates the likelihood of a positive response.

Engagement Strategy Agent - Chooses the optimal engagement type (email, SMS, push notification).

Reinforcement Learning Agent - Continuously improves offer targeting based on success metrics.

 

2. Reinforcement Learning for Optimization

The system employs Q-Learning/MARL to optimize agent activation. Over time, it learns which engagement strategies work best for different customer segments, ensuring maximum sales and retention.

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3. Cost Efficiency with Locally Trained LLMs

The locally trained LLMs refer to models that were fine-tuned and optimized in-house rather than relying entirely on cloud-based APIs. This was done to reduce dependency on expensive cloud services, improve inference efficiency, and maintain control over data privacy.

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The key techniques used for training these LLMs locally include:

• Quantization (INT4, INT8): Reducing model size while maintaining performance.

• Fine-tuning on domain-specific data: Adapting pre-trained models for customer engagement.

• Optimization for edge deployment: Ensuring the models run efficiently on local hardware, such as Mac M4.

• Using DeepSeek-Tiny: A small-scale MoE (Mixture of Experts) model tailored for customer support tasks.*

 

By training locally, we achieved lower latency, reduced cloud API costs, and better adaptability for personalized engagement strategies.

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Business Impact & Cost Savings

• Increased Sales: Personalized offers resulted in a 15-20% increase in conversions.

• Higher Retention: Customers who received tailored incentives had a 25% higher reordering rate.

• Lower AI Costs: Training LLMs locally saved 40% in cloud API expenses compared to fully cloud-hosted models.

• Faster Response Time: Optimized inference with DeepSeek-Tiny reduced offer processing latency by 30%.

Conclusion

By implementing a multi-agent AI system using reinforcement learning and dynamic agent invocation, businesses can achieve a highly adaptive and cost-efficient customer engagement strategy. This solution not only maximizes sales and retention but also optimizes AI costs through local training, quantization, and efficient orchestration using LangChain Graphs. The future roadmap includes real-time speech interaction and further reinforcement learning enhancements to improve personalization effectiveness.