At Vortex IQ, speed is strategy.

In July 2025, we set ourselves a challenge:
Could we design, build, test, and launch 10 production-ready AI agents in 10 days?

This wasn’t a marketing stunt. It was a real-world sprint to push our platform, processes, and mindset — and prove our Agent Builder Studio, MCP Server, and Execution Engine were ready for scale.

We did it.
And in the process, we learned a lot about designing AI agents at speed — without sacrificing reliability, UX, or business value.

Here’s what we built, how we did it, and the biggest lessons we’re taking forward

What We Built

Each of these 10 agents is live and in use by merchants today:

1. Product Visibility Agent – Hides out-of-stock SKUs across all channels
2. SEO Title Optimiser Agent – Updates product titles using keyword prompts
3. Discount Rule Creator Agent – Applies % discounts by brand, tag, or category
4. Metafield Cleaner Agent – Identifies and removes outdated or blank metafields
5. Image Optimisation Agent – Compresses and replaces media with AVIF/WebP
6. 301 Redirect Generator Agent – Creates and applies redirects in bulk
7. Low Inventory Notifier Agent – Sends alerts when SKUs fall below threshold
8. Price Rounding Agent – Normalises pricing to brand-specific formats (£29.99)
9. Broken Link Checker Agent – Detects and logs 404s from live product pages
10. Analytics Summary Agent – Pulls GA4 and Stripe insights into a daily Slack digest

Each agent:

• Was built in our Agent Builder Studio
• Used real merchant APIs (BigCommerce, Shopify, Stripe, GA4)
• Had testing and rollback enabled
• Was launched to live workspaces with usage tracking

How We Did It: The Process

1. Start with Use Case, Not Capability

We didn’t ask, “What can GPT-4o do?”
We asked, “What do merchants keep doing manually?”

Every agent began with a clear business goal, not just a clever prompt.

2. Schema-First Design with MCP

Before agent planning, we pulled schemas via our Model Context Protocol (MCP):

• Structured the fields (e.g. inventory_level, seo_title, discount_rule)
• Validated field types and required inputs
• Auto-generated JSON test cases

This allowed agents to reason in bounded, schema-aware context.

3. Skill Composition Over Prompt Engineering

Each agent was built from modular skills:

• get_low_stock_items
• patch_product_visibility
• calculate_discount_payload
• send_slack_alert

We didn’t write monolithic prompts.
We composed logic like LEGO blocks — safe, testable, and reusable.

4. Sandbox Before Live

Every agent was tested in staging:

• Test products
• API call logging
• Live preview and rollback enabled

We caught and fixed 4 edge-case issues before any customer saw them

5. Human-in-the-Loop QA

Each agent had a human review phase:

• Is the agent’s output accurate?
• Does it break in multi-storefront environments?
• Are logs and rollback pathways visible and functioning?

Speed didn’t mean skipping trust.

What We Learned

1. Reusability is Power

70% of logic was reused across agents.
The key? Build generic, well-named, parameterised skills.

2. Naming Matters

Agents with boring names got ignored.
Agents with clear names like “SEO Booster” or “Broken Link Fixer” got immediate adoption.

3. Rollback Builds Confidence

Teams tried agents faster when they knew they could reverse changes instantly.

4. Logging is a Feature

When logs showed exactly what an agent did and why, users trusted the outputs — even if they didn’t fully understand the API.

5. Personalisation Unlocks Stickiness

Merchants loved config files like:

yaml

CopyEdit

threshold: 10  

rounding_strategy: nearest_0.99  

priority_category: “featured”

It made agents feel theirs.

Results from the Sprint

• Average build time per agent: 92 minutes
• 10 new agents added to our marketplace
• 4 became “top used” in under 2 weeks
• 0 post-launch critical failures
• User NPS feedback: “The fastest tool we’ve adopted in years”

Final Thought

Shipping 10 AI agents in 10 days wasn’t about going fast for the sake of it.

It was about proving our framework scales, our product delivers, and our team can build agentic workflows that solve real problems — not just generate outputs.

Whether you’re an enterprise team looking to automate operations or an investor seeking platforms that scale intelligently, here’s what we showed:

• AI agents can be shipped fas
• They can be safe, modular, and reusable
• And they can deliver business value on day one

Want to build your own agent in 30 minutes?
Try it at vortexiq.ai or email [email protected]