Agentics Engineering
Multiple agent teams.
Bulletproof automation.
One agent working alone is a demo. We run several teams of AI agents side by side — each one taking work off a shared queue, building on its own branch, and answering to a review gate before anything reaches production.
The flow
How work moves through the teams
Every request, piece of feedback and idea lands in one queue. A router matches each one to the team and the model best suited to it. The teams build in parallel, and everything they produce meets at the same review gate before it ships.
Scroll the diagram sideways on a small screen.
No vendor lock
An open router, not a bet on one vendor
Every model call travels one of three inference lanes: an open router that can send a task to any AI vendor, a direct vendor connection, and a local GPU box we own. Which lane a job takes is a setting, not an architecture.
Because routing is configuration, swapping vendors — or adding a new one the week it launches — does not mean rewriting anything. No single vendor can price us, or our members, into a corner.
Open router
Sends a task to any AI vendor. New models become available to the whole fleet as soon as they exist, without a code change.
Direct vendor
A direct connection to a model provider, for the work where that provider is genuinely the best tool for the job.
Local GPU
Hardware we own and run ourselves. Work can stay on our own machines, and the price of a run is the price of electricity.
Self-evolving
The system watches its own runs
Cost per unit of work, speed, and review outcomes all feed back into the machine that produced them. Routing and technique keep moving toward cheaper, faster, better output, instead of sitting frozen at whatever we guessed on day one. Every reviewed run also writes a lesson the whole fleet reads on the next job — so an improvement found by one team is immediately available to all of them.
Each job is measured. Work that does not need an expensive model stops getting one.
Teams run in parallel, and routing learns which lane finishes a given kind of job soonest.
Review outcomes are an input, not a report. What the gate rejects changes how the next run is built.
Bulletproof
Why several teams beat one clever agent
A single agent given a large job has one plan, one attempt, and nobody checking it. We take that apart on purpose.
- Work is decomposed into cases. A big request becomes a set of small, individually reviewable pieces — each one small enough that an agent can actually finish it.
- Every case is built on an isolated branch. Teams cannot overwrite each other, and an attempt that goes wrong is thrown away instead of cleaned up.
- Nothing merges without passing a multi-reviewer gate. Several reviewers look at the same change from different angles before it is allowed near production.
- A defect routes back to an agent and writes a lesson. The fix is rebuilt properly rather than patched around, and the lesson goes to the whole fleet — so the same defect does not ship twice.
Put the teams to work
Build an AI agent quote →Tell us the work, pick a package — get pricing back the same day.
Want to build with us instead? Join the volunteer force →