Praixis AI OS · Multi-Agent Orchestration
One orchestrator running whole processes over your data.
A single agent answering questions is table stakes. The leverage is agentic workflows — systems of agents that plan, delegate, and check each other's work — plugged into your tools through custom MCP servers and coordinated by one orchestrator.
The problem
Point solutions and disconnected bots create new silos. The value shows up when agents share context and run an end-to-end process — not when there are ten of them that don't talk.
What this looks like
Custom MCP servers
We build the connectors that plug your systems — internal tools, databases, SaaS — straight into the model, hosted in your own cloud and available org-wide. Your team's AI gains hands, not just a mouth.
Output
- MCP servers for your internal systems
- Hosted in your tenant, org-wide access
- Governed, versioned, and monitored
Agentic workflows
We design systems of agents that decompose work, delegate to each other, and verify results before anything ships — autonomous within defined bounds, with humans in the loop where the stakes demand it. No auto-sending into the void.
Output
- Planner / worker / checker patterns
- Human-in-the-loop on high-stakes steps
- Guardrails and eval cases per agent
The orchestrator
One layer sits over the agents and your data spine, routing work to the right model and the right agent and keeping the whole process coherent — the always-on control room for your operation.
Output
- Central routing across agents & models
- Runs multi-step processes unattended
- Full visibility into what ran and why
Let's figure out where this fits your operation.
Most engagements start with a paid discovery — a clear map before anyone commits to a build.