A Multi-Agent Factory That Builds Automations On Demand
Hand one agent a plain-language brief. Get back a deployed, tested automation. Behind that single point of contact, a crew of around 15 specialist agents plans, builds, reviews and tests the workflow for n8n, Zapier or Make, then records what it learned so the next build goes better.
Before: every automation built by hand, one at a time.
The business was building a steady stream of automations, and every one followed the same slow cycle: understand the requirement, design the flow, wire the nodes, handle the edge cases, test it, find what broke, fix it, test again.
The cost was more than hours. Builds queued behind each other. Whoever knew the platform best became the bottleneck. Small mistakes, a wrong field mapping or a missing error branch, surfaced days later in production. And early attempts to speed things up with AI meant someone juggling a dozen chat windows, copying output between them and checking every line by hand.
The goal: one person hands a brief to one point of contact and receives a finished, tested workflow back, without babysitting the process or writing any of it themselves.
What we built: a factory with a workforce.
The system is modelled as a factory, not as one clever model doing everything.
One coordinator, about fifteen specialists
A master coordinator is the only agent a human deals with. Below it, around fifteen specialists each own one narrow job: architects who plan the build, prompt engineers, node builders for the mechanical wiring, integration builders for external services, and reviewers and testers whose only role is to check the work before it ships.
Single-skill agents, so contexts never bleed
Each agent is isolated to one skill. A node builder never sees the reviewer's notes on a different project, and a planner's context is not cluttered with wiring detail. Keeping contexts narrow is what keeps output quality steady when several projects run at once.
A defined acceptance bar
The factory judges its own work against explicit acceptance criteria and will not call a job done until it clears them. It self-tests rather than relying on the human to find failures.
How a build moves through the line
The right model for each job
Cost and quality are balanced through tiered model assignment. Heavier reasoning models sit on planning, review and prompt work, where judgement matters. Faster, cheaper models handle the mechanical building.
A memory that improves the factory
The system was proven on a live spine test: it deployed a real workflow correctly and, along the way, hit a real limitation of headless testing. It wrote that lesson into its own memory. That write-back loop, permanent per-project memory plus a librarian agent that captures gotchas, means a mistake made once is not made twice. A command centre dashboard sits over the top, with views for agents, tasks, backlog, timeline, approvals and live activity, so the whole factory can be read at a glance.
What changed: commissioning instead of building.
The business now commissions an automation by handing one brief to one agent, and receives a deployed, self-verified workflow. Human effort shrinks to the few steps that need a person. The factory runs several projects in parallel without cross-contamination, polices its own quality, and gets sharper with every job because it remembers what it learned.
It is the difference between hiring a builder for each project and owning the factory that builds them.
Day to day now.
Someone writes a brief in plain English: "When a new order lands, check stock, notify the warehouse and update the customer." The coordinator asks any clarifying questions, then the line takes over. The dashboard shows the plan, the build, the review and the tests as they happen. What comes back is a working workflow and a short punch list: add these credentials, approve this first run. That is the human part.
The same pattern, in your business.
Most businesses do not need a factory that builds automations. What they need is the principle behind it: break a messy job into narrow roles, give each role clear rules, check the work before it ships, and keep a record of what went wrong so it does not happen again.
- If you run an operations team, the same structure turns a recurring process (onboarding a new customer, closing the month, processing returns) into a line of small, checked steps instead of one person's memory.
- If you run an agency, specialist agents can draft, check and format client deliverables against your house rules before a human reviews them.
- If you are building lots of automations already, we can set up the review, testing and memory layer so every new workflow is better than the last.
Where to start is a judgement call. An AI Consultation maps your processes and tells you which ones suit this approach and which need a single, simple workflow.
AI Automation
This is AI Automation at its most ambitious: systems that build other systems, so your operations get faster with every project instead of heavier. We can stand up the same approach around the work that eats your team's hours today.