What we learned building a reliable, high‑performance AI agent platform that can handle real‑world merchant traffic.

Why We Had to Scale Fast

When we launched the Trusted AI Agent Builder, early adoption was strong. Merchants loved being able to:

• Create role‑specific AI digital workers in minutes
• Connect them to BigCommerce, Shopify, Adobe Commerce, GA4, and more
• Automate workflows that previously took hours or days

But as more merchants onboarded — and as they ran multiple AI agents at once — our concurrency requirements shot up.
We needed to handle 10,000+ active sessions, each with:

• Multiple API calls per minute
• AI reasoning tasks
• Safe execution flows with approval and rollback
• Real‑time monitoring and audit logging

Scaling wasn’t optional — it was mission‑critical.

Lesson 1: Design for Concurrency from Day One

We didn’t just “add more servers.” We:

• Adopted event‑driven architecture with asynchronous task queues
• Used container orchestration (Kubernetes) to auto‑scale agent workloads
• Leveraged message brokers (e.g., RabbitMQ) to decouple processing from execution

Takeaway: Scaling AI isn’t just about model performance — it’s about the entire orchestration layer around it.

Lesson 2: Optimise for the AI + API Combo

Every AI agent run involved both:

1. Reasoning (AI model inference)
2. Execution (API calls to e‑commerce platforms, CRMs, analytics tools)

We optimised by:

• Caching frequent prompts and reference data to reduce AI inference time
• Implementing API rate‑limit handling with smart retry logic
• Using parallel API execution where safe to cut total task time

Takeaway: In AI platforms that act on external systems, the API bottlenecks can be as critical as AI inference speed.

Lesson 3: Safety at Scale Is Non‑Negotiable

When 10,000 users can run AI agents simultaneously, one bad action could mean thousands of errors live in production.
We embedded:

• Pre‑execution simulations — agents “dry run” actions before applying changes
• Approval workflows for high‑impact changes (e.g., bulk price edits)
• Instant rollback capability for every execution

Takeaway: At scale, safety features become part of your performance strategy — because they prevent downtime and merchant churn.

Lesson 4: Real‑Time Monitoring Saves the Day

We built a live monitoring dashboard to:

• Track every agent run in progress
• Flag abnormal execution times or error spikes
• Auto‑pause problematic workflows before they cascade

This allowed us to proactively address issues before merchants even noticed.

Takeaway: You can’t fix what you can’t see — observability is essential.

Lesson 5: UX Still Matters at Scale

With thousands of users, complexity can spiral. We:

• Simplified the agent creation flow
• Added templates for common e‑commerce workflows
• Embedded usage tips and progress indicators in‑app

Result: Merchant onboarding time dropped, even as platform complexity increased behind the scenes.

Takeaway: Scaling backend capacity is pointless if the front‑end user experience can’t keep up.

The End Result

• 10,000+ concurrent users handled without downtime
• 99.97% uptime over the last 6 months
• Agent execution times cut by 35%
• Zero data loss incidents thanks to built‑in safety layers

Final Word

Scaling an AI platform isn’t just about adding GPU power — it’s about rethinking architecture, safety, and user experience for high‑traffic, real‑time environments.

At Vortex IQ, these lessons now guide every design decision we make — because as adoption grows, the stakes (and the opportunities) get bigger.