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Behind the Scenes
What makes 150+ AI agents work together seamlessly? Two years of obsessive engineering, custom tooling, and battle-tested workflows.
Common Misconceptions
The gap between using an AI chatbot and running a production AI agent system is the same gap between flying a drone and piloting a commercial aircraft.
"Just use ChatGPT"
A single general-purpose model has no memory, no project context, no quality gates. It forgets everything between sessions and hallucinates edge cases.
"AI can replace developers"
Raw AI output is unreliable. What makes it production-grade is the system around it — routing, testing, review, and human oversight at every critical junction.
"More agents = better results"
Throwing agents at a problem without orchestration creates chaos. Specialization, clear boundaries, and strict quality gates are what produce reliable output.
"It works on the first try"
Every agent, every workflow, every quality gate was refined through hundreds of real production deployments. The system you see today took two years of daily iteration.
Architecture
Each layer solves a specific problem. Together, they form a system that produces reliable, production-grade software at unprecedented speed.
Each agent is fine-tuned for its domain. Dev agents know your stack, your conventions, your file structure. QA agents know your edge cases. Design agents know your component library. No generalists — every agent is a specialist.
The hardest problem is not making one agent work — it is making 150 work together. Gareth coordinates routing, prioritization, and conflict resolution across every active project.
Nothing ships without passing through multiple layers of automated and human review. MANIAC testing tries to break every feature. Guardian reviews catch architectural mistakes. Sentinel runs continuous QA.
Most AI tools forget everything between sessions. Our agents remember — project context, past decisions, known issues, architectural patterns. Every session builds on the last.
The system is never finished. Every week, workflows are audited, agent performance is measured, and improvements are deployed. What worked three months ago has already been replaced by something better.
By the Numbers
150+
Specialized Agents
2 Years
Of Daily Refinement
50+
Custom Tools & Workflows
6
Production Products Shipped
13
Service Categories
Weekly
Improvement Cycles
Training & Leadership
An AI agent is only as good as its training, its constraints, and the human directing it. Here is how Gareth runs the system.
Every agent receives domain-specific training data, custom system prompts, and access to only the tools it needs. A frontend agent never touches database migrations. A QA agent never writes production code.
When an agent produces subpar output, the failure is traced back to its configuration and corrected. Not once — permanently. The same mistake never happens twice.
Agents execute. Humans decide. Architecture choices, UX trade-offs, and business logic are never left to automation. The system augments human expertise — it does not replace it.
Zero console errors. Zero TypeScript warnings. Full responsive testing. Accessibility checks. Performance budgets. These are not aspirations — they are gates that block deployment until met.
The system exists to ship your product faster and better. Book a call and we will walk you through exactly how it applies to your project.
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