In the past, Web applications reshaped the digital landscape by providing generic, content-driven information through centralised interfaces. However, they were largely static in terms of adaptability and personalisation, constrained by predefined workflows and rigid architectures.
Later, The rise of smartphones and native mobile applications was specifically designed with user-friendly interfaces. This enhanced usability and accessibility, giving rise to a mobile-first mindset that became foundational for modern digital strategies.
Today, Generative AI and agentic systems, with their ability to adapt and generate contextual content, are driving a shift in application design—making them more specialised, modular and autonomous interactions, taking customer experiences to a new level.
Agentic Systems in Customer Support
Agentic systems are not designed to replace traditional systems and solutions; instead, they augment them by introducing decentralised, goal-oriented workflows. These systems encapsulate logic, memory, and contextual intelligence into modular components that seamlessly integrate with existing architectures.
Here’s a breakdown of their impact in technical terms:
🎨 Personalised Customer Interface and Workflow:
- Agentic systems can create a personalised customer interface (UI) tailored to individual customer preferences and behaviours.
- By integrating front-end microservices, the agent can dynamically adjust the UI to highlight products, offers, and content most relevant to each customer.
- This integration enhances customer engagement and satisfaction by providing a seamless and customised shopping experience.
🧠 Stateful and Context-Aware Agents:
- Unlike stateless API calls that rely on transient inputs, agentic systems maintain a persistent memory layer.
- This layer, implemented using scalable databases or in-memory stores, tracks customer interactions, purchase histories, and behavioural patterns.
- By persisting state, agents can evolve their responses and actions based on longitudinal data, achieving a high degree of personalisation.
🔄 Event-Driven Architecture:
- Agentic systems excel in asynchronous and event-driven models. By leveraging messaging queues and event brokers (e.g., Kafka or RabbitMQ), they react to customer activities in real time.
- For instance, an agent can proactively notify a customer about cart abandonment or anticipated restocks based on inventory signals.
🔧 Integration with Existing Tech Stacks and Microservices:
- These systems are designed to operate within a microservices architecture, interfacing with CRMs, order management systems (OMS), and inventory APIs via well-defined frameworks (e.g., REST or gRPC).
- This decoupled design ensures that agents remain modular and reusable across multiple customer-facing touchpoints.
⚙️ Proactive Workflow Orchestration:
- Orchestration engines such as Apache Airflow or Temporal can be employed to manage agentic workflows.
- These engines coordinate tasks like retrieving customer data, querying recommendation engines, and triggering notifications, ensuring that each step of the process aligns with the customer’s context and needs.
📈 Scalability and Optimisation:
- Agentic systems are inherently distributed, allowing for horizontal scaling.
- By leveraging containerised environments (e.g., Docker) and orchestrators like Kubernetes, businesses can deploy and manage agents with fine-grained resource allocation.
- Load balancers can ensure efficient routing of customer requests to appropriate agents, maintaining high availability and performance.
Real-World Application
Consider an e-commerce platform deploying agentic systems for customer support:
- Each customer is assigned a virtual agent. The agent maintains memory of their shopping preferences, transaction history, and interaction patterns using a backend database optimised for low-latency queries (e.g., DynamoDB or Redis).
- When a customer engages, the agent dynamically retrieves context and personalises the interaction, such as suggesting products or offering discounts.
- The agent also integrates with backend systems like the OMS and CRM to provide real-time order updates, product returns, or escalate issues to human support when needed.
- Over time, the agent learns from the customer’s behaviour, fine-tuning its responses and recommendations using traditional reinforcement learning algorithms.
- An agent can operate and act autonomously, managing the entire order lifecycle with one-click acceptance, followed by payment and tracking.
Some usual queries from customers:
- Can I return my Hoover, which I bought last week, without a receipt?
- What is the status of my order?
- Please cancel my order O1312.
The agentic approach would be:
Conclusion
Agentic systems represent the next stage in the evolution of AI applications. By adopting a focused, modular, and integration-first approach, they redefine how businesses engage with customers. These systems do not aim to replace traditional AI/ML pipelines but rather complement them, much like how mobile applications complemented web platforms.
For e-commerce businesses, agentic systems offer a pathway to deliver highly personalised, scalable, and proactive customer support, ensuring a competitive edge in the ever-evolving digital landscape.
The transition to agentic-first approaches is not just a technological advancement but a strategic necessity to unlock new levels of efficiency, personalisation, and customer satisfaction—much like the shift to mobile-first paradigms over a decade ago.
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