A Multi-Agent Factory That Builds Automations On Demand
A hierarchical crew of specialist agents that takes a plain-language brief and produces working, tested automation workflows for multiple platforms, with a review loop that catches its own mistakes and a memory that compounds over time.
Where they started.
Building automations by hand is slow and does not scale. Every new workflow means the same cycle: understand the requirement, design the flow, wire the nodes, handle the edge cases, test it, fix it. The client wanted to compress that cycle by turning the building itself into an automated, agent driven process. One person should be able to hand a brief to a single point of contact and receive a finished, tested workflow back, without babysitting a dozen chat windows or writing any of it themselves.
What we did.
The system is modelled as a factory with a workforce, not a single clever model. A master coordinator is the only interface a human deals with. Below it, a crew of around fifteen specialists each own one narrow job: architects who plan the build, prompt engineers, node builders for the mechanical wiring, integration builders, and a set of reviewers and testers whose only role is to check the work before it ships.
Two deliberate design choices make it reliable. First, agents are isolated by single skill so their contexts never bleed into each other and degrade the output. Second, the system judges its own work against a defined acceptance bar and will not call a job done until it clears that bar, and it self tests rather than relying on the human to catch failures. It handles several projects at once without cross contamination, and it writes lessons back into permanent memory so it gets better over time instead of plateauing.
Work flows top down. The coordinator receives a brief and hands it to an architect, who plans the build and splits it into units. Mechanical builders assemble the pieces, integration builders wire in the external services, and reviewers and testers verify against the acceptance criteria before anything is released. Cost and quality are balanced through tiered model assignment: heavier reasoning models sit on the planners, reviewers, and prompt work where judgement matters, while faster, cheaper models handle the mechanical building. A commissioning loop runs the whole chain end to end and returns a punch list of only the items that genuinely need human hands, such as credentials or a first live run.
The system was proven on a live spine test that deployed a real workflow correctly and, in doing so, surfaced a genuine limitation of headless testing, which the system then recorded in its own memory for next time. That write back loop, permanent per project memory plus a librarian that captures gotchas, is what makes it compound. A command centre dashboard sits over the top with views for agents, tasks, backlog, timeline, approvals, and live activity, so the whole factory is observable at a glance.
What changed.
The client can commission a working automation from a single brief handed to one agent, and receive back a deployed, self verified workflow, with human effort reduced to only the steps that truly require a person. The factory runs multiple projects in parallel, 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.
Built with Agent SDK, Tiered model assignment, Live platform APIs for deploy and test, Per-project memory and write-back loop, Command centre dashboard.
AI Automation
This is AI Automation at its most compounding: automations that build other automations, so your operations get faster every month instead of heavier. We can stand up the same factory around the work that eats your team’s hours today.