Agent Command Center.
Agent apps, the agents behind them, and the models they call are three separate systems in most stacks — three places to configure, three places to fail. The Command Center is the single surface that runs all three: build and install agent apps, watch them work, and steer them when they need a human hand.
Not a chat window. A cockpit.
The engine that holds it together.
Underneath, every workflow is a directed graph: steps for LLM calls, transforms, and human approvals, wired with conditional routing and parallel branches. The engine checkpoints state so a failed run resumes instead of restarting, gates sensitive steps behind human approval, and tracks seven reliability metrics per workflow — success rate, compensation rate, human-intervention rate, model accuracy, pass confidence, tool-call latency, and completion time — exportable to your monitoring stack.
Agent apps as real software.
The backend treats agent apps the way app stores treat apps: packaged, installable units with their own identity, their own tools, and their own data. Build once, install anywhere the platform runs. The packaging and lifecycle layer is the most mature part of the stack — it is what makes an agent something you can ship instead of something you retype.
Trust, built into the protocol.
The agent layer is an agent-to-agent protocol server: agents discover each other, negotiate tasks, and hand work back and forth over an open protocol — with real identity through single sign-on instead of shared API keys. Every agent has credentials; every call is authenticated; every hand-off is auditable. The server and its integration tests are shipped and running.
Every model call, one governed path.
On Kubernetes, models deploy as labelled pods and the router resolves each request by name — adding a model is a deployment plus a routing entry, and the platform serves ours alongside anyone else's. The same governed path carries every call the workflows make: one upstream, one identity layer, one place to steer.
A control plane for training.
Agents are trained, not just prompted. The control plane launches, tracks, and audits training runs on GPU nodes: single sign-on, VPN-isolated access, full monitoring, a gateway for model traffic, and budgets that are enforced, not suggested.
Agents, apps, workflows, model serving, and training — five systems, one cockpit. The command center is where they land.