A Founder’s Retrospective from Building Vortex IQ

Every startup journey has its highlight reels — funding milestones, product launches, growth curves.

But what often goes unsaid are the mistakes that almost derailed us. The pivots we didn’t want to make. The weeks we spent fixing what we should’ve never built.

At Vortex IQ, we’ve built an AI agentic platform that transforms APIs into intelligent agents. Today we help brands like Krispy Kreme, British Airways, and BigCommerce merchants automate workflows across staging, monitoring, SEO, and performance.

But we got a lot of things wrong before we got them right.

Here are 7 key mistakes we made — and how we recovered.

1. We Built for Everyone, Not for Someone

The mistake:
We tried to build a universal AI automation tool that served developers, marketers, analysts, ops… all at once.

The result:
Bloated feature set. Confused positioning. Low activation.

How we recovered:
We laser-focused on e-commerce roles first — developers, marketers, and product managers on Shopify and BigCommerce. That focus created traction, usage, and case studies.

Lesson: Nail one vertical, one user, one value prop first.

2. We Underestimated the Complexity of APIs

The mistake:
We thought calling APIs with natural language would be simple.

The result:
Context loss, fragile prompts, hallucinating endpoints.

How we recovered:
We built our own MCP (Model Context Protocol) to structure memory, enforce schemas, and dynamically retrieve documentation for each API.

Lesson: Don’t build LLM wrappers. Build systems that reason with structure.

3. We Delayed Observability

The mistake:
We focused on feature delivery over accountability. Early agents didn’t have logs, rollback, or alerts.

The result:
Teams didn’t trust the automation. We couldn’t debug incidents quickly.

How we recovered:
We created agent dashboards with full logs, error tracing, rollback capability, and human-in-the-loop review.

Lesson: Visibility creates confidence. Build observability from day one.

4. We Took Too Long to Dogfood Our Own Agents

The mistake:
We sold agents before using them ourselves.

The result:
We missed edge cases and performance gaps.

How we recovered:
We now use our own AI agents to:

  • Write release notes 
  • Track staging issues 
  • Generate SEO metadata 
  • Optimise performance across our landing pages 

Lesson: Use what you sell. It builds empathy, speed, and credibility.

5. We Overbuilt the UI

The mistake:
We assumed users needed full dashboards to control agents.

The result:
Clunky workflows, high drop-offs, unnecessary friction.

How we recovered:
We moved to prompt-first interfaces — where users just say what they want, and the agents figure out the how.

Lesson: Let users speak their intent. Your UI should respond, not dictate.

6. We Chased Features Over Outcomes

The mistake:
We kept adding more agent capabilities — without asking: did they create impact?

The result:
Some agents were unused. Others didn’t move the needle.

How we recovered:
We implemented feedback loops tied to KPIs: revenue lift, hours saved, conversion uplift. Agents that didn’t deliver got retired.

Lesson: Ship outcomes, not features.

7. We Under-Communicated with Early Users

The mistake:
We onboarded early customers… and then disappeared into the build tunnel.

The result:
Missed upsell moments. Lost insights. Churned champions.

How we recovered:
We now schedule fortnightly check-ins, async usage reviews, and early previews of new agents for feedback.

Lesson: Early users are your best investors. Talk to them more than you think you should.

Final Thought: Mistakes Are Just Models That Need Updating

We’ve made plenty more mistakes. We’ll make new ones. That’s the cost of building something genuinely new.

But what matters is not avoiding mistakes — it’s how quickly you recover, learn, and systemise the fix.

To other founders:
Own your errors. Share your lessons. Build better, together.

Want to see how our agentic framework works in practice?
Visit vortexiq.ai or drop a line to [email protected]