What you’ll learn:
Introduction
For every AI agent trying to surface data according to customer requirements, there will be a corresponding increase in automated requests and complex interactions. This real-time data processing is expected to exponentially increase the strain on commerce systems.
The reality is that autonomous agents and AI-powered personalization engines are already generating traffic and interactions that traditional systems weren’t built to handle.
At the heart of this challenge lies scalability. Enterprises must prepare to handle high-frequency automated requests while maintaining seamless, real-time experiences for human shoppers and buyers.
The strain businesses can expect with AI agents
AI agents create two main sources of load on commerce systems: Increased backend processing and a higher demand for personalization and context.
Increased backend processing and data traffic from AI agents
Unlike human shoppers and buyers, AI agents perform rapid, multi-step searches and apply complex filters to find the best product match through multiple queries about product specifications, pricing, shipping timelines and inventory data. This behavior, which occurs in real-time, is dramatically increasing the number of API calls and database queries hitting businesses’ systems — and this is just the beginning.
AI agents don’t stop there. They also require structured and machine-readable data formats to make accurate decisions, placing further demand on systems. The need to prepare, maintain and serve this highly structured data format adds computational load for enterprises.
More than just AI agents, bot traffic and scraping are a reality that businesses need to contend with in the age of AI. While this is largely non-revenue traffic, it still consumes bandwidth and server resources, which is something enterprises must increasingly prepare for. And you don’t want your business to be in a position where agent-led load is taking down your storefronts.
Increased demand for personalization and context
As personalization and context-aware applications in AI-powered conversational commerce enter the arena, it unlocks a new level of complexity for enterprises to handle.
The more data signals businesses need to surface as part of their contextual and personalized experiences, the higher the load they need to manage as Large Language Models (LLMs) interpret complex requests, provide conversational responses and deliver tailored recommendations — all of which require significant compute resources.
Plus, agents also synthesize historical behavior, preferences and external context like weather or location. Finally, for agentic commerce, AI systems must integrate seamlessly with payment, authentication and logistics services, which may create additional operational and latency challenges.
In short, even as AI reduces routine human tasks, it fundamentally increases the computational load on commerce infrastructure. For businesses, this means that anticipating these pressures is crucial to maintain performance and availability for all customers, AI agents and humans alike.
6 scalability pillars to handle AI-driven traffic
Enterprises can take several strategic steps to handle AI-driven traffic without compromising performance:
1. Modular architecture
Breaking commerce functionality into independent services allows each component to scale independently. As opposed to monolithic applications, independent and containerized components like search, checkout and personalization can be scaled separately during traffic surges.
2. Cloud-native infrastructure
Cloud-native platforms support auto-scaling and load balancing, dynamically adjusting resources based on AI-driven demand. Multi-region or edge deployments can reduce latency and improve resilience, ensuring that high-volume agent traffic doesn’t slow down customer-facing experiences.
3. Intelligent AI-based traffic management
AI workloads can be optimized by routing requests intelligently, caching frequently accessed data, and establishing failover strategies to maintain performance during periods of high demand. This ensures that repetitive or predictable queries don’t overwhelm backend systems.
4. Monitoring and observability
Tracking metrics such as request volumes, latency, cache efficiency and error rates is essential. Observability tools enable businesses to detect and resolve bottlenecks before they affect customer experience.
5. Optimized data pipelines
Structured, machine-readable data with low-latency delivery ensures AI agents can retrieve the context they need without slowing the system.
6. Event-driven architecture
An event-driven architecture enables agents to trigger automated workflows, such as restock alerts, reorders or service updates, in real-time and at scale.
Practical next steps to prepare for AI-driven traffic
These steps act as a bridge between the strategy you outlined and how businesses can begin executing it today:
1. Audit your current traffic patterns
Identify which services see the highest demand, where latency spikes occur and how your system behaves under load generated by both humans and bots/agents.
2. Evaluate the flexibility of your current architecture
Determine whether your commerce platform can scale individual components independently and handle rapid increases in API calls without slowing down.
3. Prioritize structured, high-quality product data
AI agents rely on clean, machine-readable data. Invest in improving product attributes, metadata and real-time inventory accuracy.
4. Prepare for mixed traffic (humans + agents)
Implement caching, rate management and prioritization strategies to ensure AI-driven requests don’t degrade the human customer experience.
5. Move towards an adaptable, cloud-native commerce foundation
A modern platform, such as commercetools, allows you to scale elastically, introduce new AI-powered experiences quickly and avoid the constraints of rigid legacy systems.
6. Pilot AI-driven experiences on a modern platform
Start by integrating conversational commerce, guided selling or automated service agents into a specific business unit or region — and scale from there.
commercetools is uniquely positioned for agentic-driven scalability
Agentic commerce is reshaping the way digital interactions happen, shifting from human-initiated clicks to continuous, intelligent requests driven by AI systems. To thrive in this new landscape, enterprises need a commerce foundation that can absorb unpredictable demand, adapt quickly to new touchpoints and maintain performance even as traffic grows exponentially and unexpectedly.
This is where commercetools stands apart.
Because commercetools is built on a modern, cloud-native and API-driven architecture, businesses gain the flexibility to scale only the parts of their commerce experience that need it, respond instantly to changes in traffic patterns and integrate emerging AI technologies without overhauling their entire platform. This allows enterprises to continuously innovate while keeping their operations stable, predictable and cost-efficient.
Discover how your business can get started with agentic AI with built-in scalability with Agentic Jumpstart.
Marc Stracuzza is the Director of Product Strategy at commercetools, with 20+ years of product experience. He joined commercetools in 2020 as a Product Manager and holds a Bachelor of Science in Computer Engineering. Marc is a dynamic speaker and thought leader, known for his expertise in product strategy and innovation.
With over a decade of experience across product and marketing teams in the tech world, Julia specializes in creating innovative, customer-first strategies and driving cross-functional growth and go-to-market initiatives.