AI LAB · AGENT ORCHESTRATOR
One control plane for every agent you run.
At enterprise scale the hard part isn't the agents — it's governing their cost, their tokens and their behaviour across teams. Our orchestrator sits between your agents and the models and runs all three from one place. It works with AISDLC or any agentic flow.
Today every team wires its own agents to its own models. No shared policy, no view of who's spending which tokens, on what. It doesn't take many agents — or many people — before that's ungovernable, and the bill only compounds.
The orchestration layer is where you take it back: one gateway, one policy, fully audited — not something each team rebuilds per agent.
Between your agents and the models.
Every request from any agent passes through one orchestration layer before it reaches any model. Here's what it does in that passage — click a stage.
Four jobs, one layer.
The same four stages above — Govern, Compress, Route and Measure — in depth. Orchestration isn't a proxy; it's where the governance, efficiency, economics and observability of your whole agent fleet are decided.
Govern
Policy & guardrailsPolicy and guardrails sit on every request. Regulated and sensitive traffic is forced to the approved, governed tier, and anything outside policy is blocked — governance is locked centrally, not left to each team.
Compress
EfficiencyWe compress the context that goes into every request — fewer input tokens for the same result, before the model is ever called. The savings compound across every agent and every call.
Route
EconomicsEach request goes to the cheapest model that clears its quality bar — chosen by complexity, sensitivity, latency and modality, not by brand preference. A quality gate holds the floor; most traffic lands on economical tiers.
Measure
ObservabilityEvery prompt, response and token is logged and attributed to the agent, the team and the user — feeding cost reporting, audit trails, and the analysis that shows where to optimize next.
How it plugs into your stack.
Any agent repoints its model calls at the orchestrator. It terminates the call, normalizes it to one canonical request, runs the four responsibilities, then dispatches to the right provider — over a direct API or a CLI subprocess. The orchestrator is the trust boundary.
We don't assert the savings — we model them.
Routing turns model choice into a cost decision: most traffic lands on economical tiers, the flagship is reserved for what needs it. Rather than wave numbers around here, we built two places where you can model the economics on real prices and see the evidence.
Model it live.
The interactive policy router. Change the policy or the traffic mix and watch blended cost, escalation and quality recompute on real provider prices.
Open the router →Studies ↗See the evidence.
The experiments behind the routing and tokenomics — what we tested across models and tiers, and what actually held up in production.
Read the Studies →Fair questions. Straight answers.
What buyers ask before they commit.
Open source gives you the plumbing — a proxy that forwards calls. We give you the decisions: which model handles which request, at what cost, under which policy. It's accelerator-led, research-driven and customized to your stack — a control plane engineered for production, not a library you still have to turn into one.
It forwards each request straight to the model — no holding, no batching — so it adds only a few milliseconds. Negligible next to the model itself, which takes hundreds of milliseconds to seconds, and streaming is preserved so responses still start instantly.