A team of agents, each one specialised, each one accountable.
Orchestrate fleets of agents that plan, retrieve, execute, and verify. The whole system delivers what a single model can't.
- Pilot to production
- 0-12wk
- Throughput uplift
- 0-6×
- End-to-end success
- 0%+
- Replayable traces
- 0%
When one model isn't enough, an orchestrated team usually is.
A planner picks the strategy, a researcher gathers context, a coder or executor takes the action, a critic verifies. Each role is small, focused, and individually evaluable. The orchestrator handles routing and budgets.
- Role specialisation, planner, researcher, executor, critic
- Routing and budget control by an orchestrator agent
- Per-role evaluation, debug failures at the agent that caused them
From discovery to production.
- 01
Discover
Decompose the workflow into roles. Decide what each agent owns and where the orchestrator hands off to humans.
- 02
Prototype with evals
Build per-role evals first. The system passes only when every role passes, and the end-to-end suite passes too.
- 03
Deploy
Shipped with an orchestrator that enforces budgets, retries, and policy. Agents call your APIs; humans approve the consequential actions.
- 04
Operate
Failures get routed to the failing agent's eval set. The system improves where it actually breaks.
Stuck wiring up agents in a brittle prompt-glue mess?
Book a 30-min consultWhat you get.
A multi-agent system you can actually operate.
We deploy with explicit per-agent budgets, audit logs, and a control plane your ops team can use. Every step is traceable. Every release runs through both per-role and end-to-end evaluations.
- Per-agent cost caps, latency budgets, and retry policies
- Replayable traces with role attribution for every failure
- Continuous re-evaluation against new live traces
Common questions.
Build a multi-agent system that operates like a team.
Free 30-minute consultation. Bring a workflow, leave with an architecture.
Schedule consultation