What the graph is made of
The exact shape of the pipeline is designed around your case. These are the main elements we build it with:
They invoke an LLM for the steps that require reasoning: understanding a request, designing, writing, reviewing.
Automating doesn't always mean an LLM: where a programmatic solution is more robust, effective and economical, that's what we use.
Points where the graph stops and waits for a human action. They are what guarantees decisions stay with your team.
Deterministic checks and protections around every step: cost and tool budgets, consistency checks, detection of off-course behaviour.
At the gates, you're in charge
You've seen what you needed: give the go-ahead and the pipeline proceeds.
Something doesn't convince you: write it in a note and the agents restart from there, guided by your directions.
Before deciding you want hands-on proof: open the sandbox environment and use the application the way a user would.
All the state, in plain language
Every run exposes its shared state: the plan, the files touched, checklist progress, test results, the risk review, what it cost, and much more.
What to track, what to show and to whom: that too is defined at design time. Every organisation has different needs, so the console adapts to yours, not the other way around.
An answer for every role
Pick your seat at the table:
A tuned pipeline costs less
The more the pipeline is tuned to your specific case, the fewer mistakes the agents make: fewer wasted iterations, fewer tokens burned, less team time spent fixing things. Efficiency isn't a side effect here, it's the whole design goal.
Every run declares what it cost and what it produced: your AI investment becomes a measurable line item, not a bet. And spending limits are set upfront, so the bill never holds surprises.
Harness & loop engineering
Every node runs inside a deterministic harness: isolated sandboxes, granular permissions, cost and tool budgets enforced by the runtime, not politely requested from the model.
Iteration loops are engineered: gates sit where the cost of an error is highest, and run telemetry feeds the next tuning cycle. Your standards don't live on a wiki: they are architectural constraints of the pipeline.
The team reviews, instead of rewriting
Repetitive work moves to the agents, and people go back to the part that matters: reviewing, not rewriting. Plan, changes and assessments arrive already readable and with shared checklists.
Pull requests land in your repository through the flow you already have: no new tools to learn, no process turned upside down.