Praixis AI OS ·

Multi-Agent Orchestration

Agents that check each other's work.

For teams that tried one AI agent, watched it be right 80% of the time, and realised someone now has to check it. Forever.

A single AI agent answering questions is table stakes. The leverage is workflow automation built from 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.

Agents that check each other's workCustom MCP for your stackWhole processes, end to end

The problem

“Can't it just handle it automatically?” Yes. Then somebody checks it. Forever.

An 80%-accurate agent is impressive in a demo. Run it against a few hundred transactions a week and 80% means errors nobody catches until a customer calls. So you assign someone to review it — and you've automated a task and created a job. This is where most agent projects quietly die: nobody priced the supervision.

What this looks like

Plug the model into your systems

We build the connectors (MCP servers) that plug your internal tools, databases, and SaaS straight into the model — hosted in your own cloud, available across the company.

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.